JP2007058846A - Statistic probability model creation apparatus for lip sync animation creation, parameter series compound apparatus, lip sync animation creation system, and computer program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide lip sync animation of high quality with respect to arbitrary characters and save manpower of creation work. <P>SOLUTION: In the statistic probability model creation apparatus for lip sync animation creation, a face parameter compound part 82 includes: phonemes column estimation means 250 and 252 for estimating a phonemes column and phonemes continuation length on the basis of sound data 42 and phonemes HMM 64; a phonemes/visumes conversion part 254 for generating a visumes column on the basis of the estimated phonemes and phonemes continuation length to determine continuation length of each of the visumes; and a HMM matching part 242 for estimating a series 84 of parameters indicating traces of feature points with the visumes column as an input parameter on the basis of learned face parameter HMM 50 with respect to transition probability among the visumes and output probability at a position of each of the feature points. The lip sync animation creation system includes: a mapping part for transforming a specified face object on the basis of the series 84 of the parameters compounded by the face parameter composition part 82; and an imaging part for generating an animation from the transformed face object. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an animation creation technique using CG (Computer Graphics), a statistical probability model creation device, a parameter sequence synthesis device, and a computer program for creating a lip-sync animation that expresses a facial expression when a character speaks. In addition, the present invention relates to a lip sync animation creation system using them.

  CG is often used for the production of animation works, and animations that require advanced skills of creators in conventional cell animations can be realized by simple work. Among the techniques using CG, for example, there is a technique for producing an animation using a three-dimensional model. In this technique, the shape, position, direction, etc. of an object are defined by polygons in a virtual space in each frame of the animation. Then, based on the definition, the images of the object are synthesized and an animation is constructed from the images. Once the shape of an object is defined, images from all viewpoints can be combined any number of times for that shape.

  By transforming an object into an image for each frame, it is possible to express changes in the character's facial expression. By preparing a separate voice as the character's voice and changing the mouth shape and facial expression of the character according to the voice, it is possible to produce an animation as if the character is speaking. In this specification, changing the shape and expression of a character's mouth in accordance with the voice is called “lip sync”. In this specification, an animation realized by lip sync is referred to as “lip sync animation”.

  In order to realize the lip sync, the voice of the character and the facial expression of the character expressed by the image of each frame must be synchronized. Conventionally used techniques for realizing lip sync are classified into the following two methods. That is, one method is a method (after-recording: so-called “after-recording”) in which audio is recorded later in accordance with a video produced in advance. Another method is a method in which audio is recorded first and a video is produced later according to the audio (pre-recording: this is hereinafter referred to as “pre-recording”). In post-recording, animation creators create images for each frame while predicting changes in the facial expression of the character being uttered. The speaker (or voice actor) who is in charge of the character's voice utters the speech by adjusting the timing while watching the expression of the character on the animation. On the other hand, in Pre-Reco, the speaker speaks freely. The producer creates an image of each frame while adjusting the facial expression according to the sound.

  Various techniques for generating a lip sync animation using CG have been proposed. Non-Patent Document 1 described later discloses a technique for realizing lip sync by a technique called a key frame method. In this method, a plurality of objects expressing typical facial expressions of a character are prepared in advance. Then, using these prepared objects, the facial expression of the character being uttered is designated as follows. First, a frame (key frame) that expresses the facial expression of a character using a prepared object is determined from the frames constituting the animation. Next, the facial expression parameters used in the key frame are specified. When this specification is completed, an object representing the expression of the character in each frame of the animation is generated for each frame. At this time, for the key frame, the object designated by the above designation is applied as it is. For other frames (intermediate frames) between two key frames, an object is generated by linear interpolation on the time axis from the objects used for the two key frames before and after the intermediate frame.

  Non-Patent Document 2 described below discloses a technique for realizing lip sync through face simulation based on a physical model. In this technique, facial muscles, skin, and skeleton are physically modeled by a three-layered spring model. Manipulate the muscle on the model based on the movement of the muscle at the time of utterance, and simulate the movement of the skin when the muscle moves / deforms.

  Non-Patent Document 3 and Non-Patent Document 4 described later disclose a technique for synthesizing a moving image of a face being uttered by a statistical probability method. In this method, a face image at the time of utterance is stored in a database (hereinafter simply referred to as “DB”). An image having features suitable for the utterance content is selected from the face images in the database and reconstructed.

  Among these, in the technique described in Non-Patent Document 3, a photographic image is converted to a DB. The synthesized animation is a reconstruction of these photographic images. Therefore, if a large-scale and appropriate DB is prepared, lip sync can be realized with a natural image close to a live-action moving image.

In the technique described in Non-Patent Document 4, a three-dimensional face object is converted into a DB. In this technique, position measurement and voice recording are simultaneously performed for a plurality of predetermined points on the face during speech. Principal component analysis is performed on the position measurement data to generate facial parameters. A face parameter corresponding to a state in a phoneme hidden Markov model (HMM) prepared in advance is selected from the face parameter and voice recording data, and an average is taken for each state. Using this averaged parameter, an object corresponding to each state of the phoneme HMM is generated. Using the object generated in this manner and the phoneme HMM, an animation is synthesized by pre-recording. That is, first, an utterance voice is prepared in advance, and a phoneme string is synthesized from the utterance voice using a phoneme HMM. Based on this phoneme string, a phoneme is designated for each frame of the animation. An object corresponding to the designated phoneme is determined as an object of the frame, and a series of objects is created and imaged.
Cohen, M.C. M.M. Massaro, D .; W. 1993. “Model of simultaneous articulation in visually synthesized speech,” model and technique of computer animation, pages 139-156 (Cohen, MM, Massaro, DW 1993. “Modeling coarticulation in synthetic visual speech”, Models and Techniques in Computer Animation, pp.139-156) Waters, K.M. 1987. “A muscle model for animating three-dimensional facial expressions.” ACM SIGGRAPH '87 pp .17-24) Ezzat, T. Geiger, G .; Poggio, T .; 2002. “Learnable Video Realistic Speech Animation”, ACM Seagraph 2002 (Ezzat, T., Geiger, G. and Poggio, T. “Trainable Videorealistic Speech Animation”, Proceedings of ACM SIGGRAPH 2002) K Kakihara, S Nakamura, K Shikano “Synthesis of facial movement from speech based on HMM”, Proceedings of International Conference and Exposition of the Institute of Electrical and Electronics Engineers (IEEE), July-August 2000, Vol. 1, 427 -430 (K Kakihara, S Nakamura, K Shikano, "Speech-To-Face Movement Synthesis Based on HMMs", Proceedings of IEEE International Conference on Multimedia and Expo, July-August, 2000 Vol.1, pp.427-430 ) Tokuda Keiichi, “Basics of Speech Synthesis by HMM”, IEICE Technical Report, Vol. 100, No. 392, SP2000-74, pp. 43-50, October 2000

  Regardless of whether it is post-recording or pre-recording, lip-syncing by manual animation production requires an enormous amount of work and advanced skills. In order to achieve lip-sync with post-recording, the producer must accurately predict the facial expression in each frame at the time of utterance. However, there are limits to this prediction. In addition, in order to realize lip sync with post-recording, the speaker must adjust the timing of the utterance. However, it is difficult to adjust the utterance timing and the like in units of frames. Therefore, in order to realize advanced lip sync, both producers and speakers are required to have extremely high skills. On the other hand, in pre-recording, an image of each frame is produced in accordance with prerecorded audio. Unlike sound, images can be corrected in units of frames, so that timing can be adjusted with high accuracy. Therefore, advanced lip sync can be realized. However, this method requires the creator of the animation image to adjust the image frame by frame. Alternatively, the producer must correct the image by comparing the sound and the image. For this reason, the creator is forced to perform harsh work.

  The above-described problems related to the work for realizing the lip sync similarly occur in animation production by CG using a three-dimensional model. In order to express a facial expression or the like using a three-dimensional object, the object must be deformed in a virtual space. That is, the position of the vertex (node) of the polygon must be redefined. To create an animation by deforming an object, you must do this for each frame. Since the shape model used for the current animation is composed of an enormous number of polygons, the number of nodes that need to be redefined is also enormous. Therefore, the amount of work and cost required for production are enormous.

  In the technique described in Non-Patent Document 1, an object with a typical expression is used as it is for image synthesis in a key frame. Therefore, an object for a certain character cannot be diverted to another character. That is, an object with a typical expression must be prepared for each character. In this technique, an object that expresses a facial expression in an intermediate frame is generated by linear interpolation of an object prepared in advance. However, the change in human facial expression is not such a linear thing. Therefore, this method cannot faithfully express changes in facial expression, and lip sync is incomplete.

  The technique described in Non-Patent Document 2 is a technique that takes into account the physical structure of the face, and if the simulation is performed appropriately, it may be possible to faithfully express changes in facial expressions. However, in order to express the facial expression intended by this technique, the contraction amount of each muscle tissue must be set based on anatomical knowledge. Therefore, it is extremely difficult to create a lip sync animation using this technique.

  In the technique described in Non-Patent Document 3, the feature amount of a facial expression at the time of utterance is obtained from a moving image. However, this technique has the following problems. That is, the face and its facial expression are three-dimensional (three-dimensional), whereas the moving image is two-dimensional information. It is difficult to obtain a feature amount related to a three-dimensional shape change from a two-dimensional moving image. Therefore, this technique has a problem that it is difficult to obtain information about changes in facial expressions. In addition, the quality of moving image information depends on the performance of the camera for capturing the image. Therefore, there is a problem that an error may occur in the feature amount obtained from the moving image.

  In the method described in Non-Patent Document 4, facial expressions that can be created as animations are limited to those expressed by objects stored in the DB. In order to express various expressions of characters having various appearances, it is necessary to prepare a face object for each character and create a database. This is virtually impossible.

  SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide a statistical probability model creation device, a parameter sequence synthesis device, and a lip using the same, which realize advanced lip sync for any character and save labor for the production of lip sync animation. It is to provide a sync animation creation system.

  The statistical probability model creation device according to the first aspect of the present invention includes a plurality of predetermined voice data recorded by recording a voice at the time of utterance and a face of a speaker who is recorded at the same time when the recorded voice data is recorded. This is a statistical probability model creation device for creating a statistical probability model for creating a lip sync animation from a data set composed of motion capture data relating to individual feature points. The motion capture data includes a plurality of frames, each of the plurality of frames includes position data of a plurality of feature points in the frame, and a temporal correspondence relationship is provided between the plurality of frames and the recorded audio. . The statistical probability model creation device uses a predetermined phoneme statistical probability model prepared in advance with respect to speech feature values and phonemes, and configures a phoneme sequence included in recorded speech data included in the data set, and the phoneme sequence. Based on the phoneme sequence estimation means for estimating the phoneme duration for each phoneme, and the phoneme sequence and phoneme duration estimated by the phoneme sequence estimation means, labeling each frame with a label belonging to a predetermined label set is performed. As a statistical probability model for creating a lip sync animation by statistical learning from the labeling means to perform and the motion capture data labeled by the labeling means, the transition probability between labels and the output probability of the position of each feature point Learning means for learning a statistical probability model.

  The phoneme string and its duration are estimated from the speech at the time of utterance. Based on this phoneme sequence and phoneme duration, labeling is performed for each frame of speech and motion capture data. A statistical probability model is obtained when the learning means performs statistical learning data using the labeled motion capture data as learning data. By using this statistical probability model, when a voice that is labeled with respect to phonemes constituting the voice is given, the probability of the position of the feature point of the face corresponding to the voice can be output based on the label sequence. Based on this probability, the movement of the face can be estimated from the voice by determining the maximum likelihood of the trajectories of those feature points. Since the trajectory of feature points is given, even if the face model is different from the speaker when learning data is recorded, if the feature points are associated, the movement of the face model is estimated based on the speech can do. Therefore, the work which requires enormous labor is unnecessary. As a result, it is possible to provide a statistical probability model creating apparatus that realizes advanced lip sync for any character and saves labor in creating lip sync animation.

  The label set may include a plurality of predetermined visual element labels each representing a mouth shape at the time of speaking. The labeling unit converts the phoneme sequence estimated by the phoneme sequence estimation unit into a visual element label sequence according to a predetermined correspondence between the phoneme and the visual unit, and configures the sequence based on the phoneme duration. Based on the means for determining the continuation length of each visual element label and the sequence and the continuation length of the visual element label determined by the means for determining, a visual element label is used for each of the frames. Visual element labeling means for performing labeling.

  The speech is converted into the visual element, and the model is learned using the visual element and the motion capture data as learning data. Since the trajectory of the feature points estimated from the model is estimated in relation to a predetermined label set in the form of a visual element, the estimation can be performed efficiently.

  The number of visual element labels included in the label set may be smaller than the number of phoneme types included in the phoneme set estimated by the phoneme string estimation means.

  Since the number of visual element labels is smaller than the number of phonemes, the final feature point position can be estimated efficiently.

  The label set may include a plurality of phoneme labels, each representing one phoneme. The labeling means generates a phoneme label sequence based on the phoneme string estimated by the phoneme string estimation means, and determines the duration of each phoneme label based on the phoneme duration. Phoneme labeling means for labeling each frame based on the phoneme label based on the phoneme label sequence determined by the means and the duration.

  Phoneme labels obtained from speech are used as they are for learning. When a series of position data of facial feature points is estimated from a model, the number of combinations is enormous, but if the form of phoneme labels is taken as an output, the combinations are limited by the number of phoneme labels. As a result, it is possible to efficiently estimate the shape of the face using this model.

  The learning means learns from the motion capture data labeled by the labeling means as a learning unit using a set of three consecutive labels as a statistical probability model for creating a lip sync animation, Means for learning a statistical probability model related to the output probability of the position of the feature point may be included.

  The facial expression is influenced not only by the phoneme being uttered, but also by the phonemes before and after it. Therefore, by using a set of three consecutive labels as a learning unit in this way, when estimating a sequence of facial feature point position data based on a model, estimation is performed in a form that matches the actual continuity of speech. And the animation moves naturally.

  The statistical probability model creation device further includes, for each frame in the motion capture data, a predetermined motion of a plurality of feature points from position data of a plurality of feature points in the frame and a frame adjacent to the frame. Dynamic feature data calculating means for calculating the characteristic feature data and adding it to the corresponding position data. The learning means is labeled from the motion capture data including the position data labeled with the dynamic feature data and labeled by the labeling means. As a statistical probability model for creating a lip sync animation, a means for learning a statistical probability model related to the transition probability between labels and the output probability of the position of each feature point is included.

  When dynamic feature data is used for learning and the same dynamic feature data is used when estimating the position of a facial feature point from speech, the feature point trajectory resembles the actual trajectory. It will be smooth.

  In each frame in the motion capture data, the dynamic feature data calculation means calculates the position of the frame from the position data of the plurality of feature points of the frame and the position data of the plurality of feature points in the frame adjacent to the frame. A means for calculating velocity parameters and acceleration parameters of a plurality of feature points as dynamic feature data and adding them to corresponding position data may be included.

  When the computer program according to the second aspect of the present invention is executed by a computer, it causes the computer to operate as any one of the statistical probability model creation apparatuses according to the first aspect of the present invention.

  A parameter sequence synthesizer according to a third aspect of the present invention is a parameter sequence synthesizer for synthesizing a parameter sequence representing a trajectory of a plurality of feature points of a speaker's face at the time of utterance. The parameter sequence synthesizer receives a speech generated by utterance, and based on a first statistical probability model obtained by performing learning in advance on a speech feature and a phoneme, A phoneme sequence estimation means for estimating the phoneme duration of each phoneme constituting the phoneme sequence, and a label set defined in advance based on the phoneme sequence and the phoneme duration estimated by the phoneme sequence estimation means Label sequence generating means for determining a duration of each of the labels constituting the sequence, and a transition probability between labels and an output probability of the position of each feature point Based on the second statistical probability model obtained by learning in advance, the trajectories of a plurality of feature points are estimated using the sequence generated by the label string generation means and the duration as input parameters. By including a track estimating means for generating a parameter sequence.

  The phoneme sequence included in the speech is converted into a predetermined label sequence, and the model is learned using the label sequence and motion capture data as learning data. Since the trajectory of the feature points estimated from the model is limited by the labels in a predetermined label set, the estimation can be performed efficiently.

  The label set may include a plurality of predetermined visual elementary labels, each representing the shape of the mouth when speaking. The second statistical probability model is learned in advance regarding the transition probability between visual elementary labels and the output probability of the position of each feature point. The label sequence generation unit converts the phoneme sequence estimated by the phoneme sequence estimation unit into a sequence of visual unit labels according to a predetermined correspondence between the phoneme and the visual unit label, and based on the phoneme continuation length, Conversion means for determining the duration of each visual element label constituting the.

  The number of visual element labels included in the label set may be smaller than the number of phoneme types included in the phoneme set estimated by the phoneme string estimation means.

  Since the number of visual element labels is smaller than the number of phonemes, the final feature point position can be estimated efficiently.

  The label set may include a plurality of phoneme labels, each representing one phoneme. The second statistical probability model is obtained by previously learning the transition probability between phoneme labels and the output probability of the position of each feature point. The label sequence generation unit generates a sequence of phoneme labels based on the phoneme sequence estimated by the phoneme sequence estimation unit, and determines the duration of each phoneme label constituting the sequence based on the phoneme duration. Means for doing so.

  When estimating a series of facial feature point position data from a model, the number of combinations is enormous, but if the form of phoneme labels is taken as an output, the combinations are limited by the number of phoneme labels. As a result, even when estimating the shape of the face using this model, the estimation can be performed efficiently by obtaining the phoneme label sequence.

  The second statistical probability model includes a statistical probability model based on dynamic features previously learned with respect to transition probabilities between visual element labels, position parameters of each feature point, and output probabilities of dynamic feature parameters related to the feature points. But you can. The trajectory estimation means generates the label sequence based on the statistical probability model based on the dynamic features obtained by learning in advance regarding the transition probability between labels, the position parameters of each feature point, and the output probability of the dynamic feature parameters. Means for outputting a sequence of position parameters and dynamic feature parameters that is the maximum likelihood as a sequence of position parameters and dynamic feature parameters for a plurality of feature points using the sequence generated by the means and the duration as input parameters And the position parameter and the dynamic feature parameter series are corrected using the dynamic feature parameter by a conversion specific to the statistical probability model from which the parameter is obtained, and each of the plurality of feature points is corrected. Means for estimating the trajectory.

  It was estimated that the position parameter series and dynamic feature parameter series were obtained using the model learned including dynamic feature parameters in this way, and then the position parameter series was corrected using the dynamic feature parameter series. Later feature point movements are smooth and natural.

  When executed by a computer, the computer program according to the fourth aspect of the present invention causes the computer to operate as any one of the parameter series synthesis devices according to the third aspect of the present invention.

  The lip-sync animation creation system according to the fifth aspect of the present invention synchronizes with audio based on a predetermined face object that defines the shape of the face using the coordinate values of a plurality of nodes in the first coordinate space. A lip-sync animation creation system for creating facial animation. The lip-sync animation creation system includes a plurality of feature points of a speaker's face synthesized by any one of the parameter series synthesizer according to the third aspect of the present invention and the parameter series synthesizer for speech input. A deformed object generating means for generating an object defining the shape of the face for each frame of the animation by changing the coordinate value of the node in the face object based on the parameter series representing the trajectory of the frame, and each frame of the animation And an imaging means for synthesizing a face image in the frame from the object generated by the deformed object generating means.

  Hereinafter, a face animation creation system according to an embodiment of the present invention will be described with reference to the drawings. In the drawings used for the following description, the same parts and data are denoted by the same reference numerals. Their names and functions are also the same. Therefore, description thereof will not be repeated.

  <First Embodiment>

[Constitution]
FIG. 1 is a block diagram showing the overall configuration of a face animation creation system according to the present embodiment. Referring to FIG. 1, this face animation creation system 40 defines voice data (hereinafter simply referred to as “voice data”) 42 as a character's voice and the shape of the character's expressionless face. This is a system for creating an animation 46 in which the facial expression of the character changes (ie, lip-syncs) in synchronization with the voice of the character from the face object 44 that is data for the purpose.

  The face animation creation system 40 records the learning voice and measures the position change (hereinafter referred to as “face parameter”) that occurs in each organ of the speaker's face during the speech. Recording system 60 for simultaneous execution, speech-face parameter DB 62 for storing learning data recorded by the recording system 60, phoneme HMM 64 that models the relationship between speech and phonemes, and mouth during speech A visual element correspondence table 66 representing a correspondence relationship between a visual element (viseme) which is a minimum unit representing the shape of the phoneme and a phoneme.

  The face animation creation system 40 further uses a phoneme HMM 64 and a visual element correspondence table 66 to calculate a statistical probability that models the relationship between the mouth shape (visual element) at the time of speech and the face parameter from the speech-face parameter DB 62. A phoneme HMM 64 and a visual element correspondence table based on a learning system 68 for learning a model, a face parameter HMM 50 which is a statistical probability model obtained as a result of learning by the learning system 68, and voice data 42 and face object 44. 66 and an animation creation system 80 for creating the animation 46 using the face parameter HMM 50. The face animation creation system 40 further includes a display device 96 and an input device 98 for the user to operate the animation creation system 80.

  The animation creation system 80 uses a phoneme HMM 64, a visual element correspondence table 66, and a face parameter HMM 50 to synthesize a face parameter series 84 corresponding to the voice data 42 based on the voice data 42. 82, a mapping unit 90 for generating a face shape model 92 of the character at the time of speech based on the synthesized face parameter series 84 and the face object 44, and the mapping unit 90 An imaging unit 94 for converting the shape model 92 for each frame into an image and generating an animation 46 is included.

<Recording system 60>
FIG. 2 shows the configuration of the recording system 60. Referring to FIG. 2, the recording system 60 includes a recording / recording system 112 for recording the voice of the speaker 110 and the moving image of the speaker 110 at the time of speaking, and the face of the speaker 110 at the time of speaking. Measured by a motion capture (hereinafter referred to as “MoCap”) system 114 for measuring the position of each part and its trajectory, audio / video data 116 recorded by the recording / recording system 112, and the MoCap system 114. In order to create a data set 120 composed of voice data and face parameter data at the time of utterance from the data 118 (hereinafter referred to as “MoCap data”), and store it in the voice-face parameter DB 62. Data set creation device 122.

  The recording / recording system 112 receives microphones 130A and 130B for receiving voices uttered by the speaker 110 and converting them into voice signals, and captures a moving image of the speaker 110, and the video signal and the microphones 130A and 130B. And a camcorder 132 for simultaneously recording audio signals and generating audio / moving image data 116.

  The camcorder 132 has a function of supplying the time code 134 to the MoCap system 114. The camcorder 132 has a function of converting an audio signal and a video signal into data in a predetermined format, and adding the same time code as the time code 134 to record it on a recording medium (not shown).

  The MoCap system 114 according to the present embodiment includes an optical system that measures the position of a measurement target using the reflected light of a highly recursive optical reflection marker (hereinafter simply referred to as “marker”). The MoCap system 114 includes a plurality of infrared cameras 136 </ b> A for capturing images of infrared reflected light from markers attached to a plurality of predetermined positions on the head of the speaker 110 at predetermined time intervals. .., 136F and a data processing device 138 for measuring the position of each marker for each frame based on the video signals from the infrared cameras 136A,..., 136F, adding the time code 134 from the camcorder 132, and outputting it. Including.

  FIG. 3 schematically shows the mounting position of the marker mounted on the speaker 110. Referring to FIG. 3, markers are respectively attached to a large number of locations 160 </ b> A,. The marker has a hemispherical shape or a spherical shape, and its surface is processed to retroreflect light. The size of the marker is about several mm. In order to enrich the voice-face parameter DB 62, it is necessary to perform measurement over a plurality of days or a plurality of speakers 110. For this reason, the mounting order of the markers is determined in advance, and the position determined by the relative position with the characteristic position of the facial organ or the mounted marker is determined in advance as the mounting position. The mounting position thus determined is referred to as a “feature point” in this specification. In the example shown in FIG. 3, markers are arranged at 181 feature points 160A,.

  Due to the physical structure of the face, there is a portion on the face of the speaker 110 that has the feature that it moves following the movement of the head itself but is hardly affected by the change in the expression of the speaker 110. . For example, temples and nose tips have these characteristics. In this embodiment, such a location is also determined as a feature point. Hereinafter, such a feature point is referred to as a fixed point. It is desirable to set four or more fixed points for normalization processing to be described later.

  Referring again to FIG. 2, the data processing device 138 generates MoCap data 118 by collecting the measurement data (hereinafter referred to as “marker data”) at the positions of the markers for each frame, and the data set creation device 122. Output to. A commercially available optical MoCap system can be used for the MoCap system 114. Since the functions and operations of the infrared camera and the data processing device in the commercially available optical MoCap system are well known, detailed description thereof will not be repeated here.

  The data set creation device 122 includes an audio / video storage unit 140 for capturing and storing audio / video data 116, a MoCap data storage unit 142 for acquiring and storing MoCap data 118, and audio / video data. 116 and MoCap data 118 are extracted based on the time codes attached to them, and an extraction processing unit for outputting audio data (hereinafter referred to as “recorded audio data”) 150 and MoCap data 152 that are synchronized with each other. 144.

  The data set creation device 122 further normalizes the MoCap data 152 so as to cancel the head movement component in the extracted MoCap data 152 and converts it into a facial parameter series 154 representing changes in each organ of the face. And a normalization processing unit 146 for synthesizing and synthesizing the recorded audio data 150 and the face parameter series 154 to generate a data set 120 and storing it in the audio-face parameter DB 62. .

  The normalization processing unit 146 converts the face parameter of the frame by converting the marker data of the frame so that the position change of the fixed point becomes 0 in each frame of the extracted MoCap data 152. Has the ability to generate. In this embodiment, affine transformation is used for this transformation.

  Here, the marker data in the MoCap data 152 of the frame at time t = 0 is expressed as P = <Px, Py, Pz, 1> in the homogeneous coordinate system. The marker data at time t ≠ 0 is expressed as P ′ = <P′x, P′y, P′z, 1>. The relationship between the marker data P and the marker data P ′ is expressed by the following equation (1) using the affine matrix M.

顔パラメータの系列１５４の各フレームにおいて不動点の位置データがすべて同じ値となれば、不動点の位置変化が０になる。そこで、本実施の形態では、フレームごとに、ｔ＝０のフレームにおける各不動点のマーカデータと、処理対象のフレームにおける当該不動点のマーカデータとから、当該フレームにおけるアフィン行列Ｍを算出する。そして、アフィン行列Ｍを用いて、各マーカデータをアフィン変換する。変換後のマーカデータはそれぞれ、ｔ＝０での頭の位置のまま発話を行なった状態での顔の特徴量の位置を表すものとなる。

If the fixed point position data are all the same in each frame of the face parameter series 154, the fixed point position change is zero. Therefore, in the present embodiment, for each frame, the affine matrix M in the corresponding frame is calculated from the marker data of each fixed point in the frame at t = 0 and the marker data of the fixed point in the processing target frame. Then, each marker data is affine transformed using the affine matrix M. Each of the converted marker data represents the position of the facial feature amount in a state where the speech is performed with the head position at t = 0.

<Speech-Face Parameter DB 62>
FIG. 4 schematically shows the configuration of the data set 120 stored in the voice-face parameter DB 62 (see FIG. 1). Referring to FIG. 4, the data set 120 includes cut-out recorded audio data 150 and a face parameter series 154. The face parameter series 154 includes face parameters 170A,..., 170N for a plurality of frames. Each of the face parameters 170A,..., 170N corresponds to any time within a period during which the voice represented by the recorded voice data was spoken. That is, by referring to the recorded voice data 150 and the face parameters 170A,..., 170N, information on the position change of the feature point when an utterance having a certain feature is performed can be obtained.

<Phoneme HMM64>
A phoneme HMM 64 shown in FIG. 1 is an HMM related to the characteristics of speech provided for each phoneme. FIG. 5 shows an outline of the phoneme HMM 64. Referring to FIG. 5, phoneme HMM 64 outputs a likelihood that a corresponding phoneme exists in the speech when given a predetermined parameter (hereinafter referred to as “speech parameter”) 180 representing the feature of speech. Has function. Accordingly, by using the phoneme HMM 64, the phoneme sequence 182 and the phoneme duration of each phoneme constituting the phoneme sequence can be estimated from the speech parameter 180. In the present embodiment, MFCC (Mel-Frequency Cepstral Coefficient) is used as the audio parameter 180.

<Visual Element Correspondence Table 66>
The visual element correspondence table 66 shown in FIG. 1 is a table showing the correspondence between phonemes and visual elements. FIG. 6 shows the structure of the visual element correspondence table 66. Referring to FIG. 6, the visual element correspondence table 66 represents a correspondence relationship between 10 types of visual elements representing the shape of the mouth during speech and 43 types of phonemes. For example, the visual element “A” represents the shape of the mouth when the phoneme “a” or “A” is spoken. The shape of the mouth when speaking the phoneme “h” depends on the shape of the mouth when speaking the preceding and following phonemes. Therefore, it is represented by the symbol “***” separately from the 10 types of visual elements corresponding to this phoneme.

<Learning system 68>
FIG. 7 is a block diagram showing the configuration of the learning system 68 (see FIG. 1). Referring to FIG. 7, the learning system 68 includes a preprocessing unit 202 for generating a data set used for learning the face parameter HMM 50 from the data set 120 in the speech-face parameter DB 62, and a learning data set 200. A learning DB 204 for storing and an HMM learning unit 206 for learning the face parameter HMM 50 from the learning data set 200 stored in the learning DB 204 are included.

  The preprocessing unit 202 selects a data set 120 to be processed from the voice-face parameter DB 62, and recorded audio data 150 (see FIG. 5) in the data set 120 selected by the data set selection unit 210. 4), the visual element for estimating the sequence of visual elements corresponding to the utterance content when the data set 120 is recorded and the duration of each visual element using the phoneme HMM 64 and the visual element correspondence table 66. The face parameters 170A,..., 170N (see FIG. 4) included in the face generation unit 212 and the face parameter series 154 in the selected data set 120 are labeled with labels representing visual elements for learning. And a labeling unit 214 for generating the data set 200.

  The visual element sequence generation unit 212 extracts a speech parameter 180 (see FIG. 5) from the recorded speech data 150, and a phoneme corresponding to an utterance based on the extracted speech parameter 180. The phoneme sequence estimation unit 222 for estimating the maximum likelihood for each frame by using the phoneme HMM 64, and the phonemes constituting the phoneme sequence 182 estimated by the phoneme sequence estimation unit 222 in the visual unit correspondence table 66 And a phoneme / visual element conversion unit 224 for converting into a visual element based on this.

  The feature amount extraction unit 220 has a function of extracting the MFCC vector in each frame as the audio parameter 180 from the recorded audio data. The phoneme sequence estimation unit 222 has a function of estimating the maximum likelihood phoneme sequence 182 and the phoneme duration from the phoneme HMM 64. The phoneme string estimation unit 222 of the present embodiment uses a Viterbi algorithm for this estimation. That is, the phoneme sequence estimation unit 222 estimates a Viterbi sequence of phonemes that outputs a given MFCC vector sequence. The phoneme / visual element conversion unit 224 has a function of converting each phoneme constituting a Viterbi sequence of estimated phonemes into a visual element. The labeling unit 214 associates the temporal change of the visual element during the utterance with the face parameter for each of the data sets 120.

  The HMM learning unit 206 learns a sequence of face parameters 170A,..., 170N and its likelihood when a predetermined visual element sequence is given, using the face parameters 170A,. It has a function to do. However, the facial expression during utterance may change depending on the preceding and following visual elements, as in the articulation combination in the relationship between phonemes and speech parameters. In learning a phoneme HMM for speech recognition, a triphone may be used as a recognition processing unit to cope with articulation coupling. Therefore, in the present embodiment, learning of the face parameter HMM 50 is performed using a triple visual element (TriViseme) in which three visual elements are one set as a processing unit.

<Animation creation system 80>
(Face parameter synthesis unit 82)
FIG. 8 is a block diagram showing the configuration of the face parameter synthesis unit 82 (see FIG. 1). Referring to FIG. 8, the face parameter synthesis unit 82 uses a phoneme HMM 64 and a visual element correspondence table 66 to convert the audio data 42 into a visual element sequence. The HMM matching unit 242 for synthesizing the face parameter sequence 84 when the voice to be played is uttered using the visual element sequence generated by the visual element sequence generation unit 240 and the face parameter HMM 50 is included.

  The visual element sequence generation unit 240 includes a feature amount extraction unit 250, a phoneme sequence estimation unit 252, and a phoneme / visual element conversion unit 254. These functions are the same as the feature amount extraction unit 220, the phoneme sequence estimation unit 222, and the phoneme / visual element conversion of the learning system 68 shown in FIG. 7, except that the feature amount extraction unit 250 receives the input of the speech data 42. The function of the unit 224 is the same. Therefore, description of these functions will not be repeated.

  The HMM matching unit 242 receives the visual element sequence and its duration from the visual element sequence generation unit 240, and generates a face parameter series 84 having the maximum likelihood for the entire utterance represented by the visual element sequence and the duration. And a function of synthesizing using the face parameter HMM50.

(Mapping unit 90)
FIG. 9 is a block diagram showing the configuration of the mapping unit 90 (see FIG. 1). 9, the mapping unit 90 is connected to an input device 98 and a display device 96, and is a virtual marker (hereinafter simply referred to as a feature marker 160A,..., 160M (see FIG. 3) on the face object 44. (Referred to as “virtual marker”) in accordance with a user operation, and a marker labeling unit 272 for labeling each node in the face object 44 with a virtual marker close to each node. And a marker labeling data storage unit 274 for storing marker labeling data representing the correspondence between the nodes formed by the labeling by the marker labeling unit 272 and the virtual markers.

  The mapping unit 90 further uses the marker labeling data stored in the marker labeling data storage unit 274 and the face parameter series 84 synthesized by the face parameter synthesis unit 82 to calculate the shape of the face represented by the face object 44 from the face shape. A face object deforming unit 276 for sequentially creating deformed face objects 92 is included.

  The virtual marker placement unit 270 sets the coordinates of each feature point on the coordinate system that defines the face object 44 in accordance with the virtual marker placement operation performed by the user using the input device 98 and the display device 96. By setting the coordinates of the feature points in this way, each marker data of each feature point can be assigned to the position of each virtual marker on the face object 44. At this time, conversion between the coordinate system of the face parameter and the coordinate system of the face object is also performed.

  FIG. 10 shows an example of the face object 44 and the virtual marker. Referring to FIG. 10, a face object 44 is a shape model that represents a predetermined face shape in a stationary state by a large number of polygons (polygons) each surrounded by a black line segment in this figure. . A vertex of the polygon (intersection of edges) is a node in the face object 44. Generally, the face has cuts that do not constitute a face, such as eyes, mouth, and nostrils. These cuts are generally not modeled as part of the face object 44. That is, no polygon is defined at the cut. Alternatively, it is defined as an object different from the face object 44. Therefore, the cut and the face are partitioned by the boundary edge.

  The shape of the face expressed by the face object 44 may be any one created by the user. However, in order to give a facial expression to the face object 44 using the face parameter, it is necessary to define which part of the shape represented by the face object 44 is each organ of the face. For this purpose, the virtual marker placement unit 270 (see FIG. 9) places the virtual markers 300A,..., 300M on the face object 44 in accordance with the user's operation.

  At this time, the user is guided so that the virtual markers 300A,..., 300M are arranged according to the mounting order of the markers used for recording the motion capture data in the recording system 60 (see FIG. 2). Therefore, the virtual marker can be arranged at an appropriate position while reflecting the user's subjectivity. The virtual marker placement unit 270 illustrated in FIG. 9 outputs the coordinates of each virtual marker in the coordinate system that defines the face object 44 to the marker labeling unit 272.

  The marker labeling unit 272 selects a node to be processed from the nodes of the face object 44, and selects a virtual marker having the closest distance from the selected node (hereinafter referred to as “selected node”) as a virtual. Select based on marker coordinates. Then, it is determined whether the selected virtual marker (hereinafter referred to as “selected marker”) is appropriate as a virtual marker associated with the selected node. If it is appropriate, the selected marker is adopted as the corresponding marker of the selected node, and if it is inappropriate, it is rejected. Such a process is repeated to employ a predetermined number n (for example, n = 3) of virtual markers. In this specification, a virtual marker adopted for a certain node is called a “corresponding marker” of the node.

  In the present embodiment, the boundary edge of the face object is used as a reference when determining whether the selected marker is appropriate as a corresponding marker.

  FIG. 11 is a flowchart showing the structure of the marker labeling process executed by the marker labeling unit 272. Referring to FIG. 11, when the process is started, the process from step 342 to step 354 surrounded by step 340 </ b> A and step 340 </ b> B is executed for each node in face object 44.

  In step 342, the distance from the selected node to the virtual marker is calculated. Furthermore, a list of virtual markers sorted in ascending order of the distance is displayed. In step 344, 0 is substituted into a variable i for controlling the following repetition and a variable j representing the number of corresponding markers employed. In step 346, 1 is added to the variable i.

  In step 347, it is determined whether or not the value of the variable i exceeds the number Mmax of virtual markers. If the value of the variable i exceeds Mmax, an error is assumed and the process is terminated. Normally, this is not the case, but such error handling is provided just in case. If the value of variable i is less than or equal to Mmax, control proceeds to step 348.

  In step 348, the line segment connecting the virtual marker (hereinafter referred to as “marker (i)”) existing at the position indicated by the variable i from the top of the list and the selected node is any boundary in the face object 44. It is determined whether or not the constraint that the edge does not cross is satisfied. If the line segment crosses one of the boundary edges, the process returns to step 344. Otherwise, go to step 350.

  In step 350, the marker (i) at this point is designated as one of the corresponding markers of the selected node. That is, information indicating the marker (i) is stored as marker node correspondence information of the selected node. Thereafter, the control proceeds to step 352. In step 352, 1 is added to the variable j. In step 354, it is determined whether or not the value of the variable j is 3. If the value of the variable j is 3, the process proceeds to step 340B. Otherwise, go to step 344.

  As described above, the line segment connecting the selected node and the virtual marker that crosses the boundary edge of the face object is excluded from the virtual marker corresponding to the node. This is due to the following reason. For example, there may be a boundary edge between the upper lip and the lower lip. In this case, the node located on the upper lip and the node located on the lower lip move differently. Therefore, for example, when calculating the movement amount of the upper lip node, it is not appropriate to use the movement amount of the marker existing on the lower lip. Whether or not the line segment crosses a certain boundary edge is determined by, for example, whether the boundary edge is shared by two polygons constituting the face object or belongs to only one.

  FIG. 12 shows polygons around the lips and a virtual marker in the face object 44. Hereinafter, a method for identifying the corresponding marker of the node will be described using a specific example with reference to FIG. Referring to FIG. 12, a large number of triangular polygons exist around the lips of face object 44. Each polygon is surrounded by three edges. A boundary edge 400 exists between the upper lip and the lower lip. The boundary edge corresponds to a tangent line between the face object 44 and the cut line or an outer edge of the face object 44. Therefore, edges other than the boundary edge are shared by the two polygons, but the edge corresponding to the boundary edge 400 is not shared.

  First, the marker labeling unit 272 selects one node from the nodes constituting the face object 44. This node is the selection node. Here, it is assumed that the node 410 shown in FIG. 12 is a selection node. In the vicinity of the selection node 410, virtual markers 412A,..., 412E exist. The marker labeling unit 272 calculates the distance between the selected node 410 and the virtual marker based on the coordinates of the node 410 and the coordinates of the virtual marker. Then, the virtual marker 412A located closest to the node 410 is selected from the virtual markers.

  Subsequently, the marker labeling unit 272 checks whether or not the line segment 414A connecting the selection node 410 and the virtual marker 412A crosses the boundary edge 400. This line segment 414A does not cross the boundary edge 400. Therefore, the marker labeling unit 272 sets the virtual marker 412A as one of the corresponding markers of the selection node 410. The virtual marker 412B next to the virtual marker 412A and next to the node 410 is selected from the virtual markers, and the inspection is performed. A line segment 414B connecting the selection node 410 and the virtual marker 412B crosses the boundary edge 400. Therefore, the virtual marker 412B is excluded from the corresponding marker of the selection node 410.

  The marker labeling unit 272 repeats the above operation until a predetermined number (three) of corresponding markers are selected, and the corresponding markers of the node 410 (virtual markers 412A, 412D, and 412E in the example shown in FIG. 12) are displayed. select.

  Referring to FIG. 9 again, face object deformation unit 276 assigns each marker data in the face parameter of a certain frame to the virtual marker. Further, the face object deforming unit 276 changes the amount vector v calculated by a predetermined interpolation formula from the amount of change of the corresponding virtual marker to each node of the face object 44 based on the marker labeling data in the marker labeling data storage unit 274. Is assigned to the face object 44. Then, the deformed face object 44 is output as the shape model 92. When the coordinate of the node of the face object 44 is N, the coordinate of the virtual marker corresponding to the node is Mi, and the coordinate of the marker in the shape model 92 that is the deformed face object is M′i, the face object deforming unit 276. Calculates the change vector v of the coordinate of the node by the following interpolation formula (2).

［動作］
本実施の形態に係る顔アニメーションの作成システム４０は以下のように動作する。

[Operation]
The face animation creation system 40 according to the present embodiment operates as follows.

<Operation of recording system>
Hereinafter, an operation in which the recording system 60 performs recording and generates the data set 120 will be described. Referring to FIG. 2, markers are previously attached to the feature points 160 </ b> A,..., 160 </ b> M (see FIG. 3) of the head of the speaker 110. In that state, the speaker speaks. In order to enrich the voice-face parameter DB 62 or to include each phoneme in a well-balanced manner, the content of the utterance is determined in advance, and the utterer 110 is uttered with the content. You may make it receive.

  When the recording starts and the speaker 110 speaks, the recording / recording system 112 records the voice and the moving image of the face at the time of speaking as follows. That is, the microphones 130A and 130B receive the voice of the speaker 110 and generate a voice signal. The camcorder 132 captures a moving image of the speaker 110 who is speaking and records the video signal simultaneously with the audio signals from the microphones 130A and 130B to generate the audio / moving image data 116. At this time, the camcorder 132 supplies the time code 134 to the MoCap system 114 and assigns the same time code as the time code 134 to the audio / video data 116.

  At the same time, the positions of the feature points 160A,..., 160M at the time of utterance are measured by the MoCap system 114 as follows. Each marker moves following the movement of the corresponding feature point. Each of the infrared cameras 136 </ b> A,..., 136 </ b> F captures infrared reflected light from the marker at a predetermined frame rate (for example, 120 frames per second) and outputs the video signal to the data processing device 138. The data processing device 138 assigns a time code 134 to each frame of the video signal, and calculates the position of each marker for each frame based on the video signal. The data processing device 138 collects the data of the positions of the markers for each frame and accumulates them as MoCap data 118.

  The audio / moving image data 116 and the MoCap data 118 recorded by the above recording process are given to the data set creation device 122. The data set creation device 122 accumulates the audio / video data 116 in the audio / video storage unit 140 and accumulates the MoCap data 118 in the MoCap data storage unit 142.

  The cut-out processing unit 144 first reads out the MoCap data in the frame at t = 0 from the MoCap data storage unit 142 and gives it to the normalization processing unit 146. The data of this frame is used for normalization by the normalization processing unit 146. Subsequently, the extraction processing unit 144 extracts the recorded audio data 150 from the audio / video data 116 stored in the audio / video storage unit 140 in a predetermined unit such as one utterance. Then, the position of the recorded audio data 150 on the time code is specified with reference to the time code given to the extracted recorded audio data 150, and the recorded audio data 150 is given to the combining unit 148. Subsequently, the cut-out processing unit 144 cuts out the MoCap data 152 from the MoCap data 118 at the same position as the recorded audio data 150 on the time code, and provides it to the normalization processing unit 146.

  In each frame of the MoCap data 152, the normalization processing unit 146 obtains an affine matrix from the fixed point marker data of the frame and the fixed point marker data of the frame of t = 0 given in advance. Each marker data is affine transformed using an affine matrix. By this conversion, the converted marker data represents the position of the facial feature amount in the state where the utterance is performed with the head at the head position at t = 0. As a result, the MoCap data 152 becomes a face parameter series 154. The face parameter series 154 is provided to the combining unit 148.

  The combining unit 148 generates the data set 120 (see FIG. 4) by synchronizing and combining the recorded audio data 150 and the face parameter series 154 and stores them in the audio-face parameter DB 62.

<Learning of face parameter HMM50>
Hereinafter, an operation in which the learning system 68 learns the face parameter HMM will be described. Referring to FIG. 7, each of the data sets 120 (see FIG. 4) in the voice-face parameter DB 62 is converted into a learning data set 200 by the preprocessing unit 202 of the learning system 68 as follows. .

  That is, first, the data set selection unit 210 selects the data set 120 (see FIG. 4) to be processed from the voice-face parameter DB 62. Then, the recorded audio data 150 and the face parameter series 154 included in the data set 120 are provided to the visual element sequence generation unit 212 and the labeling unit 214, respectively.

  When the recorded speech data 150 is given to the visual element sequence generation unit 212, the feature amount extraction unit 220 extracts the MFCC for each frame as a vector series 180 of the feature amount of the speech from the recorded speech data 150. The phoneme sequence estimation unit 222 estimates the phoneme sequence 182 (see FIG. 5) corresponding to the extracted MFCC vector sequence by the Viterbi algorithm based on the phoneme HMM64. That is, from the given vector sequence, the phoneme string 182 having the maximum likelihood in the entire utterance and the phoneme duration of each phoneme constituting the phoneme string 182 are estimated. The phoneme / visual element conversion unit 224 converts each phoneme constituting the estimated phoneme string 182 into a visual element. Thereby, 43 types of phonemes are grouped into 10 types of visual elements. Accordingly, the number of possible combinations of visual element sequences 208 output by the visual element conversion unit 224 is smaller than the number of combinations of phoneme sequences 182 that can be input to the visual element conversion unit 224. The data output by the phoneme / visual element conversion unit 224 represents visual elements corresponding to the mouth shape when the speaker 110 speaks at each time of the data set 120.

  The labeling unit 214 performs labeling on the face parameters 170A,..., 170N in the face parameter series 154 based on the visual element sequence. Correspondence between the temporal change of the visual element at the time of utterance and the face parameter is performed for each data set 120. The labeling unit 214 generates a learning data set 200 composed of face parameters 170A,..., 170N labeled with visual elements, and stores this in the learning DB 204.

  The HMM learning unit 206 learns the face parameter HMM 50 using the learning data set 200 stored in the created learning DB 204. At this time, the HMM learning unit 206 learns the face parameter HMM 50 using a triple visual element, which is a set of three visual elements, as a processing unit, and outputs transition probabilities between visual elements and face parameters 170A,. Learning about the probability is performed, and the face parameter HMM 50 is formed.

  By learning the face parameter HMM 50 as described above, it is possible to synthesize a series of face parameters from a visual element sequence based on the face parameter HMM 50. The face parameter represents the position of many face feature points 160A,..., 160M (see FIG. 3) in each frame. The visual element represents the shape of the mouth when speaking. Therefore, if the visual element of each frame character corresponding to the voice of the character on the animation is specified, a large number of faces in each frame are obtained using the visual element sequence composed of the visual element and the face parameter HMM50. The position information of the feature points 160A,..., 160M can be synthesized. That is, it is possible to estimate the trajectory of the feature points 160A,. Therefore, it becomes possible to obtain information regarding not only the shape of the mouth at the time of speech but also the change in facial expression.

  Moreover, there are fewer types of visual elements than phonemes. Therefore, an HMM in which a state is provided for each visual element is a model having a smaller number of states than an HMM in which a state is provided for each phoneme. It is considered that the expression of the speaker who is speaking changes depending on the shape of the mouth that speaks rather than the phoneme. Therefore, the quality of the face parameter HMM50 learned from the visual element sequence is not inferior to the quality of the face parameter HMM50 learned from the phoneme sequence or from the MFCC sequence. When learning from the same amount of learning data, it is more accurate to learn a model with a small number of states because there is not enough data for learning in some areas. The problem that it is impossible to estimate) does not occur and is efficient. Therefore, by learning the face parameter HMM 50 from the visual element sequence, an efficient and high-quality face parameter HMM can be obtained. Furthermore, since the HMM learning is performed using the triple visual element as a processing unit, it is possible to perform highly accurate learning with respect to changes in facial expressions depending on the preceding and subsequent visual elements.

(Composition of face parameters)
The operation of the animation creation system 80 shown in FIG. 1 will be described below. Voice data 42 representing the voice of the character is prepared and provided to the face parameter synthesis unit 82 shown in FIG. The voice data 42 is obtained by recording in advance what is spoken by a speaker (or voice actor) who is in charge of the voice of the character. Alternatively, it may be voice data synthesized by a voice synthesis technique. When the audio data 42 is input to the face parameter synthesis unit 82, the visual element sequence generation unit 240 configures the visual element sequence and the visual element sequence from the audio data 42 using the phoneme HMM 64 and the visual element correspondence table 66. Estimate the duration of each visual element. This operation is the same as the operation of the visual element generation unit 212 (see FIG. 7) of the learning system 68. Thereby, the change of the shape of the mouth at the time of the speech of the voice represented by the voice data 42 is specified.

  The HMM matching unit 242 performs matching between the visual element sequence generated by the visual element sequence generation unit 240 and the face parameter HMM 50, and synthesizes the maximum likelihood face parameter series 84 for the entire utterance.

  The face parameter series 84 synthesized by the face parameter synthesis unit 82 as described above is obtained from a change in the shape of the mouth during the speech of the voice expressed by the voice data 42. Therefore, this series 84 represents the trajectory of the facial feature points 160A,..., 160M when the speech is uttered. Therefore, not only the shape of the mouth at the time of utterance but also the nonlinear change in the position of each feature point of the face can be specified by the synthesized face parameter series 84.

  The face parameter synthesis unit 82 synthesizes a face parameter series 84 from the voice data 42 by two-stage estimation used for the phoneme HMM 64 and the face parameter HMM 50. That is, the face parameter series 84 that can be output in response to the input of the speech parameter 180 of the speech data 42 is narrowed down by estimation of the phoneme string 182 based on the phoneme HMM 64. Further, by converting phonemes to visual elements, the face parameter series 84 that can be output is further narrowed down. Therefore, even if there are many feature points, the face parameter series 84 can be efficiently synthesized.

  However, the face parameters synthesized by the face parameter synthesis unit 82 are synthesized based on the face parameter series 154 stored in the voice-face parameter DB 62 shown in FIG. That is, it represents the facial expression change when the speaker 110 in the recording system 60 shown in FIG. 2 speaks the voice equivalent to the voice represented by the voice data 42. Therefore, the mapping unit 90 according to the present embodiment generates a shape model 92 corresponding to each frame at the time of utterance from the face object 44 representing the shape of the character's face and the face parameter series 84 as follows. Generate.

(Generation of shape model 92 by mapping)
Referring to FIG. 9, when face object 44 (see FIG. 4) is given to mapping unit 90, first, face object 44 is given to virtual marker placement unit 270, marker labeling unit 272, and face object deformation unit 276. It is done.

  The virtual marker placement unit 270 places the virtual markers 300A,..., M on the face object 44 according to the user's operation. Thereby, the position of the feature points 160A,..., 160M (see FIG. 3) on the coordinate system of the face object 44 in the face object 44 in the expressionless state is specified. That is, the virtual marker placement unit 270 first images the face object 44 and outputs it to the display device 96, and further receives designation of the position of the virtual marker on the initial face model from the user via the input device 98. The position of the virtual marker on the face object 44 is specified according to the same rules as the marker placement on the speaker in the recording system 60. Therefore, the positional relationship between the face object 44 and each virtual marker corresponds to the positional relationship between the face of the speaker 110 (see FIG. 2) and the marker attached to the speaker 110.

  The virtual marker placement unit 270 performs coordinate conversion from the coordinate system of the motion capture data to the coordinate system of the face model for the marker data of each marker, and specifies the coordinates of each virtual marker in the coordinate system of the initial face model. The virtual marker placement unit 270 gives the coordinates of each virtual marker to the marker labeling unit 272.

  The marker labeling unit 272 receives the face object 44 and the coordinates of the virtual marker, and for each node of the face object 44, the three corresponding markers of the node are as described above with reference to FIGS. To identify. The marker labeling unit 272 determines corresponding markers for all nodes, creates marker labeling data representing the corresponding markers for the nodes, and stores them in the marker labeling data storage unit 274 together with the coordinates of each virtual marker.

  The face object deforming unit 276 creates the shape model 92 in each frame as follows based on the face parameter series 84, the face object 44, and the marker labeling data.

  When the face object transformation unit 276 is provided with one frame of 84 from the face parameter series, the face object transformation unit 276 reads the marker labeling data from the marker labeling data storage unit 274, and based on the position of each feature point in the face parameter, The position of each node in the shape model 92 is calculated as follows.

  That is, the face object deforming unit 276 first acquires the coordinates of the virtual marker on the face object 44 from the marker labeling data storage unit 274. Each virtual marker has a corresponding relationship with a feature point in the face parameter. Therefore, the face object deforming unit 276 assigns the position of the feature point corresponding to the virtual marker to each virtual marker based on the data for one frame in the face parameter series 84, and the one frame worth. The coordinates of each virtual marker after the change are calculated.

  Furthermore, the face object deforming unit 276 calculates the amount of change of one node based on the coordinates of the n corresponding markers designated for the node by the above interpolation formula (2). The face object deforming unit 276 executes this process for each node of the face object 44 for each frame. Thereby, the coordinates of each node are changed, and a deformed face shape model 92 is generated for each frame. The face object deforming unit 276 gives each of the deformed face shape models 92 to the imaging unit 94.

(Create animation by imaging)
When receiving the deformed face model for each frame, the imaging unit 94 performs a predetermined rendering process such as adding a texture to the face model. An image generated by this processing becomes an image of each frame in the animation 46. A moving image formed by the images of these frames is an animation 46.

  As described above, the mapping unit 90 according to the present embodiment associates many feature points of the speaker's face with each node of the face object 44. Further, the trajectory of the face object 44 is calculated based on the measurement data for each feature point. Therefore, a temporal change of the face object as a set of nodes is obtained as the face parameter series 84, whereby the animation 46 can be created. The face parameter series 84 represents a non-linear trajectory of each feature point of the face when the voice represented by the voice data 42 is uttered. Therefore, it is possible to create a natural animation that faithfully reproduces non-linear changes in facial expressions during speech.

  The animation creation system 80 of this embodiment creates an animation on a model basis. The user simply prepares voice data 42 corresponding to the voice of the character and a face object 44 that defines the shape of the character's face in a stationary state, and specifies the feature points on the face object 44 according to the rules. Create natural lip-sync animations that change facial expressions according to the character's voice. Further, the shape of the character's face represented by the face object 44 may be arbitrary without limiting the design of the character's face. Therefore, a lip sync animation can be created without narrowing the variation of animation production by the user.

[Realization and operation by computer]
Each functional unit of the face animation creation system 40 according to the present embodiment is a computer except for some special devices included in the recording / recording system 112 and the MoCap system 114 of the recording system 60 (see FIG. 2). It is realized by hardware, a program executed by the computer hardware, and data stored in the computer hardware. FIG. 13 shows the external appearance of the computer system 500, and FIG. 14 shows the internal configuration of the computer system 500.

  Referring to FIG. 13, a computer system 500 includes a computer 510 having an FD (flexible disk) drive 522 and a CD-ROM (compact disk read only memory) drive 520, a keyboard 516, a mouse 518, and a monitor 512. including.

  14, in addition to the FD drive 522 and the CD-ROM drive 520, the computer 510 includes a hard disk 524, a CPU (Central Processing Unit) 526, a CPU 526, a hard disk 524, an FD drive 522, and a CD-ROM. A bus 536 connected to the drive 520, a read-only memory (ROM) 528 for storing a bootup program and the like, and a random access memory (RAM) connected to the bus 536 for storing a program command, a system program, work data, and the like ) 530. Computer system 500 further includes a printer 514.

  Although not shown here, the computer 510 may further include a network adapter board that provides a connection to a local area network (LAN).

  A computer program for causing the computer system 500 to implement each functional unit of the face animation creation system 40 is stored in the CD-ROM 532 or FD 534 inserted in the CD-ROM drive 520 or FD drive 522 and further transferred to the hard disk 524. Is done. Alternatively, the program may be transmitted to the computer 510 through a network (not shown) and stored in the hard disk 524. The program is loaded into the RAM 530 when executed. The program may be loaded directly into the RAM 530 from the CD-ROM 532, the FD 534, or via a network.

  This program includes a plurality of instructions for causing the computer 510 to realize each functional unit of the face animation creation system 40 of this embodiment. Some of the basic functions necessary to realize this function are provided by modules of various toolkits installed in the computer 510, or an operating system (OS) or a third-party program operating on the computer 510. . Therefore, this program does not necessarily include all functions necessary for realizing the system and method of this embodiment. This program executes processing performed by each functional unit of the above-described face animation creation system 40 by calling an appropriate function or “tool” in a controlled manner so as to obtain a desired result. It is only necessary to include an instruction to be executed. The operation of computer system 500 is well known and will not be repeated here.

  In the above embodiment, in the learning system 68 (see FIG. 7), the labeling unit 214 performs the labeling based on the visual element sequence converted by the phoneme / visual element conversion unit 224 and the duration of each visual element. It was. However, the present invention is not limited to such an embodiment. For example, the labeling unit 214 may perform labeling based on the phoneme sequence 182 estimated by the phoneme sequence estimation unit 222 and the phoneme duration. In this case, the HMM learning unit 206 learns the face parameter HMM from the phoneme string 182 and the phoneme duration. Further, in this case, the HMM matching unit 242 of the face parameter synthesis unit 82 shown in FIG. 8 performs the face parameter based on the phoneme sequence 182 and the phoneme duration length estimated by the phoneme sequence estimation unit 252 of the face parameter synthesis unit 82. Matching with the HMM 50 is performed.

  Further, in the system according to the present embodiment, the positions and numbers of the facial feature points 160A,..., 160M are not limited to those shown in FIG. However, as the number of feature points used for mapping increases, the facial expression change in the animation 46 is more accurately and naturally expressed. In addition, as the number of feature points increases, the synchronization of the lip sync improves. The animation creation system 80 may output the shape model 92 in each frame instead of outputting the animation 46. In this way, it is possible to generate an animation by combining the shape model 92 with another object or the like.

<Second Embodiment>
According to the face animation creation system 40 according to the first embodiment, it is possible to automatically create an animation from sound. However, as described below, there are points to be further improved, for example, mouth movement becomes unnatural.

  FIG. 15 (A) shows the original mouth movement obtained from the face image at the time of speech, and FIG. 15 (B) shows the facial motion using the face animation creation system 40 according to the first embodiment. Shows mouth movements when automatically creating an animation of an image. In FIG. 15A, the movement of the mouth is smooth. On the other hand, the movement of the mouth in the animation shown in FIG. 15 (B) roughly matches that shown in FIG. 15 (A), but there are many stepped steps in detail. I understand. This indicates that how to open the mouth changes in a step shape on the animation image. Therefore, when you see this animation, you feel a little unnatural. Such a problem is considered to occur because a vector that determines each position of the face image obtained by the face parameter HMM50 is an average vector in each state of the face parameter HMM50 determined by matching.

  In order to make the motion of the step-like animation image smoother, for example, a low-pass filter may be applied to the parameter series of the face image, or approximation by a spline curve may be performed on the parameter series. However, when such measures are taken, there is a problem that sharpness is lost from the obtained image and natural movement cannot be obtained.

  In the second embodiment, in order to solve such a problem, learning of the face parameter HMM is performed using not only the position of the facial feature point but also dynamic feature parameters such as velocity and acceleration thereof. As will be described later, by using the face parameter HMM learned including the dynamic feature parameters in this way, smooth, sharp and natural face movements resembling the movement of the original face image are obtained. can get. The dynamic feature parameter is a feature amount widely used in the field of speech recognition.

  The principle of learning the face parameter HMM in the second embodiment and the method for determining the position of each feature point of the face image using the face parameter HMM will be described below. Note that the HMM learning using dynamic features and the method for calculating the position vector after matching by the HMM described below are the same as those taught in Non-Patent Document 5.

When the same number of feature points as those used in the first embodiment are used as the facial feature points used for learning, information on speed and acceleration is used in addition to the position vector, so parameters per feature point are used. The number (vector number) is three times the number of vectors in the first embodiment. Let c _t be a static position vector (based on the position of a feature point in an expressionless face) at a certain time t, and τ be a sampling interval. In this case, the velocity vector .DELTA.c _t and the acceleration vector delta ² c _t of the feature point at time t is approximated generally as follows.

ただしＬ^（１）及びＬ^（２）はそれぞれ、時刻ｔにおける速度及び加速度の算出において、時刻ｔの前後で考慮すべき位置ベクトル及び速度ベクトルを含む時間幅をサンプリング時間τを単位として表したものであり、ｗ_１及びｗ_２はそれぞれ、各時刻での速度ベクトル及び加速度ベクトルを算出するために使用する、位置ベクトル及び速度ベクトルに割当てる重みを示す。本実施の形態では、Ｌ^（１）＝Ｌ^（２）＝１とし、また重みｗ_１としては、連続する３つの重みとしてｗ_１＝［−０．５，０，０．５］という値を用い、重みｗ_２としては同様にｗ_２＝［０．２５，−０．５，０．２５］を用いる。

However, L ⁽¹⁾ and L ⁽²⁾ respectively represent the time width including the position vector and the velocity vector to be taken into consideration before and after the time t in the calculation of the velocity and acceleration at the time t in units of the sampling time τ. And w ₁ and w ₂ indicate weights assigned to the position vector and the velocity vector, respectively, used to calculate the velocity vector and the acceleration vector at each time. In this embodiment, L ⁽¹⁾ = L ⁽²⁾ = 1, and the weight w ₁ has a value of w ₁ = [− 0.5, 0, 0.5] as three consecutive weights. used, likewise _w 2 = [0.25, -0.5,0.25] used as the weight _{w 2.}

At this time, it is assumed to represent the output vector o _t of the HMM as follows: a sequence of output vector o _t is intended to refer at O.

式（１）（２）は、行列形式で表すと次のように書くことができる。

Expressions (1) and (2) can be written as follows in matrix form.

ベクトルｃ_ｔがＭ次元とすれば、Ｃ，Ｏはそれぞれ、ＴＭ次元及び３ＴＭ次元である。行列Ｗは、３ＴＭ行ＴＭ列の重み行列である。行列Ｗの要素の一部は係数１、ｗ_１（τ）、及びｗ_２（τ）であり、他の大部分の要素は０である。

If the vector _{c t} is the M-dimensional, C, O, respectively, a TM dimension and 3TM dimension. The matrix W is a weight matrix of 3TM rows and TM columns. Some of the elements of the matrix W are coefficients 1, w ₁ (τ), and w ₂ (τ), and most other elements are zero.

Wherein under the condition of formula (3), the probability that the output vector series O consisting output vector o _t is obtained to determine the matrix C that maximizes the objective. In general, it has been found that such C can be determined by solving the following linear equation.

ただし、行列Ｍ、Ｕはそれぞれ、以下のように表される。

However, the matrices M and U are respectively expressed as follows.

μ_ｑｔとＵ_ｑｔとはそれぞれ、ＨＭＭの状態ｑ_ｔの平均ベクトルと共分散行列とである。

μ _qt and U _qt are the mean vector and covariance matrix of the state q _t of the HMM, respectively.

Equation (4) can be solved with a computation amount of O (TM ³ L ² ) using Cholesky decomposition or QR decomposition. However, L = 1 here.

In this way, by obtaining the arithmetic expression for calculating the C from the series O output vector o _t, as long obtained sequences of output vector, static vector C corresponding thereto, i.e., the position vector of the feature point of the face Obtainable. In calculating the value of the position vector, not only the position vector of the original face image but also dynamic features such as a velocity vector and an acceleration vector are used, so that it is compared with the first embodiment as will be described later. As a result, a smoother face image can be obtained.

[Constitution]
Referring to FIG. 16, a face animation creation system 640 according to the second embodiment includes a recording system 60, a voice-face parameter DB 62, and a learning system of the face animation creation system 40 according to the first embodiment. 68, instead of the face parameter HMM 50 and the animation creation system 80, the recording system 660 having the ability to process the velocity vector and acceleration vector as data in addition to the position vector of the facial feature points as described above, the sound Includes a face parameter DB 662, a learning system 668, a face parameter HMM 650, and an animation creation system 680, thereby creating an animation 646 that is more natural and smoother than the animation 46 shown in FIG. In FIG. 16, parts that are the same as the parts of the face animation creating system 40 shown in FIG. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated here.

  As can be seen from FIG. 16, the animation creation system 680 differs from the animation creation system 80 shown in FIG. 1 in that the phoneme HMM 64 and visual elements are based on the voice data 42 instead of the face parameter synthesis unit 82 in FIG. Using the correspondence table 66 and the face parameter HMM 650, a face parameter corresponding to the audio data 42 and a parameter series 684 determined in consideration of the velocity vector and the acceleration vector are synthesized and given to the mapping unit 90 A face parameter synthesis unit 682 is included. In other respects, the animation creation system 680 has the same configuration as the animation creation system 80 shown in FIG.

  FIG. 17 shows a detailed configuration of the recording system 660 according to the second embodiment. Referring to FIG. 17, recording system 660 has a configuration similar to that of recording system 60 shown in FIG. The difference is that, instead of the data set creation device 122 of FIG. 2, the voice / moving image data 116 recorded by the recording / recording system 112 and the MoCap data 118 measured by the MoCap system 114 are used for the voice data and the time of utterance thereof. This includes a data set creation device 722 for creating a data set 720 composed of face parameter data including the dynamic feature parameters and storing it in the voice-face parameter DB 662.

  The configuration of the data set creation device 722 is also almost the same as the configuration of the data set creation device 122 shown in FIG. However, the data set creation device 722 receives the face parameter series 154 output from the normalization processing section 146 after the normalization processing section 146 shown in FIG. 2 and receives each feature point at each time included in the face parameter series 154. A dynamic feature calculation unit 746 that calculates a velocity vector and an acceleration vector of each feature point at each time using the above-described weights w1 and w2, and outputs a face parameter series 754 including the dynamic feature. 2 is received, instead of the combining unit 148 shown in FIG. 2, the face parameter series 754 including the dynamic features is received from the dynamic feature calculating unit 746, the recorded audio data 150 received from the clipping processing unit 144, The data parameter 720 including the dynamic features is generated by synchronizing and combining the face parameter series 754 including the dynamic features received from the dynamic feature calculation unit 746. And, voice - in and that it includes a coupling portion 748 for storing the facial parameter DB662, is different from the data set creation unit 122.

  FIG. 18 schematically shows a configuration of a data set 720 including dynamic features output from the combining unit 748. Referring to FIG. 18, a data set 720 including dynamic features is a facial parameter including dynamic features obtained by combining a dynamic feature and a series of facial parameters instead of the simple facial parameter series 154 shown in FIG. 4. Is different from the data set 120 in that it includes a series 754.

  In addition to the face parameters 170A,..., 170N for a plurality of frames shown in FIG. 4, the face parameter series 754 including dynamic features is a speed vector parameter (hereinafter referred to as “speed parameter”) calculated from the face parameters of these frames. .) 772A,..., 772N, and acceleration vector parameters (hereinafter referred to as “acceleration parameters”) 774A,. These face parameters 170A,..., 170N, speed parameters 772A,..., 772N and acceleration parameters 774A,. These are synchronized with the recorded audio data 150 as in the data set 120 of FIG. That is, when the recorded voice data 150 and the face parameters 170A,..., 170N, the speed parameters 772A,..., 772N, and the acceleration parameters 774A,. Information about the position of the feature point, its velocity, and acceleration can be obtained.

  FIG. 19 shows a block diagram of the learning system 668. Referring to FIG. 19, learning system 668 generally has the same configuration as learning system 68 shown in FIG. 7. The difference is that, instead of the preprocessing unit 202 shown in FIG. 7, the same as the preprocessing unit 202, the data set 720 including dynamic features can be processed to output a learning data set 700. A point including a preprocessing unit 802, a point including a learning DB 804 for storing a learning data set 700 including dynamic features, instead of the learning DB 204 in FIG. 7, and an HMM learning unit illustrated in FIG. Instead of 206, a learning data set including dynamic features stored in the learning DB 804 is used, and an HMM learning unit 806 for learning the face parameter HMM 650 is included.

  The preprocessing unit 802 has the same configuration as the preprocessing unit 202 shown in FIG. 7, but instead of the data set selection unit 210, data having a function of selecting the processing target data set 720 from the voice-face parameter DB 662. The face parameter 170A,..., 170N, the speed parameter 772A,..., 772N and the acceleration parameter 774A, which are included in the face parameter series 754 including the dynamic characteristics in the selected data set 720. .., 774N (see FIG. 18) are labeled with visual element labels output from the phoneme / visual element conversion unit 224, and include a labeling unit 814 for generating a learning data set 800. Different from the pre-processing unit 202.

FIG. 20 simply shows the configuration of the face parameter HMM 780 corresponding to one visual element after learning by the HMM learning unit 80 is performed. As shown in FIG. 20, this face parameter HMM 780 is an HMM of three states S1 to S3, and each of the states S1 to S3 has outputs o _i = (c _i , Δc _i , Δ ² c _i ) (i = 1-3) and a probability distribution giving a transition probability. By obtaining a sequence of face parameters HMM780 that maximizes the likelihood of giving such an output sequence by matching the sequence of given outputs o _i and the face parameter HMM780, the face parameters at each time are obtained at that time. Determined by the corresponding HMM. By calculating the matrix C from the face parameters using the above-described equation (4), it is possible to obtain the coordinates of facial feature points that smoothly change in consideration of dynamic feature amounts.

  FIG. 21 shows a more detailed configuration of the face parameter synthesis unit 682 shown in FIG. Referring to FIG. 21, face parameter composition unit 682 has a configuration very similar to face parameter composition unit 82 of the first embodiment shown in FIG. The difference is that, instead of the HMM matching unit 242 in FIG. 8, the voice represented by the voice data 42 is uttered by matching the visual element sequence generated by the visual element sequence generating unit 240 with the face parameter HMM 650. A point including an HMM matching unit 842 for generating and outputting an output parameter series 844 from the face parameter HMM 650 and an output parameter series 844 including a dynamic feature amount output from the HMM matching unit 842. On the other hand, in order to output the position vector series of feature points in consideration of the motion vector and the acceleration vector, that is, the face parameter series 684 (matrix C in the formula (4)) by performing the conversion using the above formula (4). The conversion unit 846 is further included.

  The HMM matching unit 842 receives the visual element sequence and its duration from the visual element sequence generation unit 240, and has a dynamic feature that maximizes the likelihood of the entire utterance represented by the visual element sequence and the duration. A function of synthesizing a series of output parameters 844 including quantities using the face parameter HMM 650 is provided.

[Operation]
Of each part of the face animation creation system 640 according to the second embodiment, the operation of a part that is the same as or corresponds to the part in the face animation creation system 40 of the first embodiment is It is the same. However, the difference is that dynamic data is included in the data to be handled. Hereinafter, the operation of the face animation creation system 640 will be described with emphasis on points different from the operation of the system 40 according to the first embodiment.

<Operation of recording system>
Referring to FIG. 17, a marker is previously attached to each feature point 160 </ b> A,..., 160 </ b> M (see FIG. 3) of the head of speaker 110. In that state, the speaker speaks. Recording is started, and the recording / recording system 112 records the voice at the time of utterance and the moving image of the face.

  The audio / video data 116 and the MoCap data 118 recorded by the above recording process are given to the data set creation device 722. The data set creation device 722 stores the audio / video data 116 in the audio / video storage unit 140 and stores the MoCap data 118 in the MoCap data storage unit 142.

  The cut-out processing unit 144 first reads out the MoCap data in the frame at t = 0 from the MoCap data storage unit 142 and gives it to the normalization processing unit 146. The data of this frame is used for normalization by the normalization processing unit 146. Subsequently, the extraction processing unit 144 extracts the recorded audio data 150 from the audio / video data 116 stored in the audio / video storage unit 140 in a predetermined unit such as one utterance. Then, the position of the recorded audio data 150 on the time code is specified with reference to the time code given to the extracted recorded audio data 150, and the recorded audio data 150 is given to the combining unit 748. Subsequently, the cut-out processing unit 144 cuts out the MoCap data 152 from the MoCap data 118 at the same position as the recorded audio data 150 on the time code, and provides it to the normalization processing unit 146.

  In each frame of the MoCap data 152, the normalization processing unit 146 obtains an affine matrix from the fixed point marker data of the frame and the fixed point marker data of the frame of t = 0 given in advance. Each marker data is affine transformed using an affine matrix. By this conversion, the converted marker data represents the position of the feature amount of the face when the utterance is performed while keeping the head position at the position of t = 0. As a result, the MoCap data 152 becomes a face parameter series 154. The face parameter series 154 is given to the dynamic feature calculation unit 746.

The dynamic feature calculation unit 746 calculates the weights w1 = [− 0.5, 0, 0.5] and the weights w ₂ = [0.25, −0.5, 0.25] is used to calculate the dynamic feature amount (velocity vector and acceleration vector) at each time and is combined with the face parameter series 154, and the face parameter series 754 including the dynamic features is combined with the combining unit 148. To give.

  The combining unit 748 generates a data set 720 including the dynamic features by synchronizing the recorded voice data 150 and the face parameter series 754 including the dynamic features, and stores the data set 720 in the speech-face parameter DB 662.

<Learning of face parameter HMM650>
First, the data set selection unit 810 selects a data set 720 to be processed from the voice-face parameter DB 662. Then, the recorded audio data 150 included in the data set 720 and the face parameter series 754 including dynamic features are provided to the visual element sequence generation unit 212 and the labeling unit 814, respectively.

  The visual element sequence generation unit 212 operates in the same manner as in the first embodiment, estimates a phoneme sequence corresponding to speech, generates a visual element sequence 208 including visual elements corresponding to each phoneme, This is given to the labeling unit 814. Based on the visual element sequence 208, the labeling unit 814 applies to each face parameter 170A, ..., 170N, speed parameter 772A, ..., 772N, and acceleration parameter 774A, ..., 774N in the face parameter series 754 including dynamic features. Perform labeling. Correspondence between temporal changes of visual elements at the time of speech and face parameters including dynamic features is performed for each data set 720. The labeling unit 814 generates a learning data set 800 labeled with visual elements and stores it in the learning DB 804.

  The HMM learning unit 806 learns the face parameter HMM 650 using the learning data set 800 stored in the created learning DB 804. At this time, the HMM learning unit 806 learns the face parameter HMM 650 by using a triple visual element, which is a set of three visual elements, as in the case of the first embodiment.

  By learning the face parameter HMM 650 as described above, it is possible to synthesize a series of face parameters including dynamic features from the visual element sequence based on the face parameter HM 650.

(Composition of face parameters)
Hereinafter, the operation of the animation creation system 680 shown in FIG. 16 will be described. Voice data 42 representing the voice of the character is prepared and provided to the face parameter synthesis unit 682 shown in FIG. Referring to FIG. 21, when the speech data 42 is input to the face parameter synthesis unit 682, the visual element sequence generation unit 240 uses the phoneme HMM 64 and the visual element correspondence table 66 to generate visual sequence and The duration of each visual element constituting the visual element sequence is estimated. This operation is the same as the operation of the visual element generation unit 212 (see FIG. 7) of the learning system 68. Thereby, the change of the shape of the mouth at the time of the speech of the voice represented by the voice data 42 is specified.

  The HMM matching unit 842 performs matching between the visual element sequence generated by the visual element sequence generation unit 240 and the face parameter HMM 650, and synthesizes the maximum likelihood face parameter series 844 for the entire utterance. The face parameter series 844 is given an average vector and a covariance matrix of each state of the HMM selected by matching by the HMM matching unit 842 when each face parameter is output, and is given to the conversion unit 846.

  The conversion unit 846 performs an operation according to the equation (4) using the average vector and the covariance matrix associated with the face parameter included in the given face parameter series 844, A face parameter matrix C after conversion is calculated, and a face parameter series 684 after conversion is output.

  The face parameter series 684 synthesized by the face parameter synthesizing unit 682 as described above is obtained from a change in mouth shape during the speech of the voice expressed by the voice data 42. Unlike the case of the first embodiment, this series 684 is synthesized from the HMM learned using not only the position vector of the facial feature point but also its velocity vector and acceleration vector. Accordingly, it is considered that the face parameter series 684 can create an animation more smoothly than the animation synthesized by the face animation creation system 40 according to the first embodiment, and it will be described later that such an effect can be actually obtained. It was confirmed as follows.

  If the face parameter series 684 is created, the animation 646 can be created by the mapping unit 90 and the imaging unit 94 shown in FIG. 16 in the same manner as in the first embodiment.

<Effects of Second Embodiment>
FIG. 22 is a diagram in which the mouth movement of the animation synthesized by the face animation creation system 640 according to the second embodiment is added to FIG. 15 as FIG. 22A and 22B are the same views as FIGS. 15A and 15B, respectively.

  Comparing FIG. 22C and FIG. 22B, in FIG. 22C, the change on the step that existed in FIG. 22B is removed, and the graph is smooth as a whole. In addition, the graph is not simply rounded, and it can be seen that a graph having a peak shape very similar to FIG. 22A is obtained.

  That is, by using the face parameter HMM780 that has been learned including not only the position vector of the facial feature point at the time of utterance but also the dynamic features of the velocity vector and the acceleration vector as in the present embodiment. It can be seen that an animation of the speaker's face can be created from the voice, and an animation that is smooth and faithful to the actual movement of the speaker's face can be created.

  In the second embodiment, the difference between the feature point position vectors is used when calculating the velocity vector and acceleration vector of the facial feature points during learning. However, the present invention is not limited to such an embodiment. If an apparatus capable of measuring a velocity vector with high accuracy is available, the velocity vector may be directly measured instead of being calculated from the position vector. In this case, the acceleration vector can be calculated from the difference between the velocity vectors.

  Similarly to the velocity vector, the acceleration vector may be directly measured by using an apparatus that can directly measure the acceleration vector.

  The embodiment disclosed herein is merely an example, and the present invention is not limited to the above-described embodiment. The scope of the present invention is indicated by each claim in the claims after taking into account the description of the detailed description of the invention, and all modifications within the meaning and scope equivalent to the wording described therein are intended. Including.

It is a block diagram which shows the structure of the whole system which concerns on embodiment of this invention. 2 is a diagram illustrating a configuration of a recording system 60. FIG. It is the schematic which shows the position of the feature point in the speaker 110. FIG. 2 is a schematic diagram showing a configuration of a data set 120. FIG. It is a figure which shows the outline | summary of phoneme HMM. It is a figure which shows an example of the visual element correspondence table. 2 is a block diagram showing a configuration of a learning system 68. FIG. 4 is a block diagram showing a configuration of a face parameter synthesis unit 82. FIG. 3 is a block diagram illustrating a configuration of a mapping unit 90. FIG. It is a figure which shows an example of the face object 44 and a virtual marker. It is a flowchart which shows the structure of the process in which the virtual marker arrangement | positioning part 270 performs the labeling by the virtual marker of each node. It is a figure which shows typically the relationship between the selection node in the face object 44, a boundary edge, and a corresponding marker. It is a figure which shows an example of the external appearance of the computer system which implement | achieves the function of the learning system 68 and the animation production system 80 which concern on embodiment of this invention. It is a block diagram of the computer system shown in FIG. It is a figure which shows the movement of the mouth by the animation synthesize | combined by the system which concerns on the 1st Embodiment of this invention in contrast with the movement of an actual mouth. It is a block diagram which shows the whole structure of the production system 640 of the face animation which concerns on the 2nd Embodiment of this invention. 3 is a block diagram illustrating a schematic configuration of a recording system 660. FIG. It is a figure which shows the structure of the data set 720 produced by the data set production apparatus 722 of the recording system 660. FIG. It is a block diagram which shows the structure of the learning system 668 shown in FIG. It is a figure which shows the relationship between the schematic structure of the face parameter HMM780, and the output parameter in each state. FIG. 17 is a block diagram showing a more detailed configuration of the face parameter synthesis unit 682 shown in FIG. 16. The mouth movement in the face animation synthesized by the face animation creation system 640 according to the second embodiment is synthesized by the actual mouth movement and the face animation creation system 40 according to the first embodiment. It is a figure shown in contrast with the movement of the mouth in the made animation.

Explanation of symbols

40,640 Animation creation system 42 Audio data 44 Face object 46,646 Animation 50,650 Face parameter HMM
60,660 Recording system 62,662 Voice-face parameter DB
64 phoneme HMM
66 Visual Element Correspondence Table 68, 668 Learning System 80, 680 Animation Creation System 82, 682 Face Parameter Synthesis Unit 90 Mapping Unit 94 Imaging Unit 110 Speaker 112 Recording / Recording System 114 MoCap System 122, 722 Data Set Creation Device 130A, 130B Microphone 132 Camcorder 136 Infrared camera 138 Data processing device 140 Audio / video storage unit 142 MoCap data storage unit 144 Extraction processing unit 146 Normalization processing unit 148, 748 Combined unit 150 Recorded audio data 160A, ..., 160M Feature point 170A, ..., 170N Face parameter 202, 802 Pre-processing unit 204, 804 Learning DB
206, 806 HMM learning unit 210, 810 Data set selection unit 212, 240 Visual element sequence generation unit 214, 814 Labeling unit 220, 250 Feature amount extraction unit 222, 252 Phoneme sequence estimation unit 224, 254 Phoneme / visual element conversion unit 242 , 842 HMM matching unit 270 Virtual marker placement unit 272 Marker labeling unit 274 Marker labeling data storage unit 276 Face object deformation unit 746 Dynamic feature calculation unit 772A,..., 772N Speed parameter 774A,.
846 Conversion unit

Claims (15)

From the data set consisting of recorded voice data obtained by recording the voice at the time of utterance and motion capture data regarding a plurality of predetermined feature points of the utterer's face recorded at the time of recording the recorded voice data, A statistical probability model creation device for creating a statistical probability model for creating a sync animation,
The motion capture data includes a plurality of frames, each of the plurality of frames includes position data of the plurality of feature points in the frame, and a temporal correspondence relationship between the plurality of frames and the recorded audio. Is attached,
The statistical probability model creation device includes:
Using a predetermined phoneme statistical probability model prepared in advance with respect to the feature amount and phoneme of the speech, the phoneme sequence included in the recorded speech data included in the data set, and the phoneme continuation regarding each phoneme constituting the phoneme sequence Phoneme sequence estimation means for estimating the length;
Labeling means for labeling each of the frames with a label belonging to a predetermined label set, based on the phoneme string and the phoneme duration estimated by the phoneme string estimation means;
As a statistical probability model for creating the lip sync animation by statistical learning from the motion capture data labeled by the labeling means, a statistical probability model relating to the transition probability between the labels and the output probability of the position of each feature point And a statistical probability model creating apparatus including learning means for performing learning. The label set includes a plurality of predetermined visual element labels, each representing a mouth shape at the time of speaking,
The labeling means is
In accordance with a predetermined correspondence between phonemes and visual elements, the phoneme string estimated by the phoneme string estimating means is converted into the visual element label series, and the series is constructed based on the phoneme duration. Means for determining the duration of each of said visual element labels;
Visual element labeling means for labeling each of the frames with the visual element label based on the sequence and duration of the visual element labels determined by the determining means. Item 2. The statistical probability model creation device according to Item 1. The statistical probability model creation apparatus according to claim 2, wherein the number of visual element labels included in the label set is smaller than the number of phoneme types included in the phoneme set estimated by the phoneme string estimation unit. The label set includes a plurality of phoneme labels, each representing one phoneme,
The labeling means is
Means for generating a sequence of the phoneme labels based on the phoneme sequence estimated by the phoneme sequence estimation means, and determining the duration of each of the phoneme labels based on the phoneme duration;
The phoneme labeling means for labeling each of the frames with the phoneme label based on the phoneme label sequence and duration determined by the means for determining. The statistical probability model creation device described.   The learning means learns from the motion capture data labeled by the labeling means as a learning unit by using a set of three consecutive labels as a statistical probability model for creating the lip sync animation. 5. The statistical probability model creating apparatus according to claim 1, further comprising means for learning a statistical probability model related to a transition probability and an output probability of the position of each feature point. The statistical probability model creation device further includes, in each frame in the motion capture data, pre-determining the plurality of feature points from position data of the plurality of feature points in the frame and a frame adjacent to the frame. Dynamic feature data calculation means for calculating and adding the determined dynamic feature data to the corresponding position data,
The learning means is a statistical probability model for creating the lip-sync animation as a statistical probability model for creating the lip sync animation by statistical learning from the motion capture data including the position data labeled with the dynamic feature data and labeled by the labeling means. The statistical probability model creation apparatus according to any one of claims 1 to 5, further comprising means for learning a statistical probability model related to a transition probability and an output probability of the position of each feature point. The dynamic feature data calculation means includes, in each frame in the motion capture data, position data of the plurality of feature points of the frame and position data of the plurality of feature points in a frame adjacent to the frame. The statistical probability model creation according to claim 6, further comprising means for calculating velocity parameters and acceleration parameters of the plurality of feature points in the frame as the dynamic feature data and adding them to corresponding position data. apparatus.   A computer program that, when executed by a computer, causes the computer to operate as the statistical probability model creation device according to any one of claims 1 to 7. A parameter sequence synthesizer for synthesizing a parameter sequence representing a trajectory of a plurality of feature points of a speaker's face at the time of utterance,
Based on the first statistical probability model obtained by receiving in advance the speech generated by utterance and learning the speech feature and phoneme in advance, the phoneme sequence for outputting the speech and the phoneme sequence are configured. Phoneme string estimation means for estimating the phoneme duration of each phoneme;
Based on the phoneme sequence estimated by the phoneme sequence estimation means and the phoneme duration, a sequence composed of labels belonging to a predetermined label set defined in advance is generated, and the continuation of each of the labels constituting the sequence Label sequence generation means for determining the length;
Based on the second statistical probability model obtained by previously learning the transition probability between the labels and the output probability of the position of each feature point, the sequence generated by the label sequence generation means and the duration A parameter sequence synthesizing apparatus, comprising: a trajectory estimating means for generating the parameter sequence by estimating trajectories of the plurality of feature points as input parameters. The label set includes a plurality of predetermined visual element labels, each representing a mouth shape at the time of speaking,
The second statistical probability model is learned in advance with respect to the transition probability between the visual elementary labels and the output probability of the position of each feature point;
The label sequence generation unit converts the phoneme sequence estimated by the phoneme sequence estimation unit into a sequence of the visual unit labels according to a predetermined correspondence between the phoneme and the visual unit label, and the phoneme duration length 10. The parameter sequence synthesizing device according to claim 9, further comprising conversion means for determining a continuation length of each visual element label constituting the sequence based on the above. The parameter series synthesis device according to claim 10, wherein the number of visual element labels included in the label set is less than the number of phoneme types included in the phoneme set estimated by the phoneme string estimation unit. The label set includes a plurality of phoneme labels, each representing one phoneme,
The second statistical probability model is obtained by previously learning the transition probability between phoneme labels and the output probability of the position of each feature point,
The label sequence generation unit generates a sequence of the phoneme labels based on the phoneme sequence estimated by the phoneme sequence estimation unit, and each of the phoneme labels constituting the sequence based on the phoneme duration 10. The parameter sequence synthesizer according to claim 9, further comprising means for determining a continuation length of. The second statistical probability model includes statistical probabilities based on dynamic features learned in advance with respect to transition probabilities between the visual element labels, positional parameters of the feature points, and output probabilities of dynamic feature parameters related to the feature points. Including models,
The trajectory estimation means includes
Generated by the label sequence generation unit based on a statistical probability model based on the dynamic features obtained by learning in advance regarding the transition probability between the labels, the position parameter of each feature point, and the output probability of the dynamic feature parameter Means for outputting a sequence of position parameters and dynamic feature parameters that is a maximum likelihood as a sequence of the position parameters and the dynamic feature parameters for the plurality of feature points using the sequence and duration as input parameters When,
The position parameter and the dynamic feature parameter series are corrected using the dynamic feature parameter by a conversion specific to the statistical probability model from which the parameter is obtained, and the plurality of feature points 13. The parameter sequence synthesizer according to claim 12, comprising means for estimating each of said trajectories.   A computer program that, when executed by a computer, causes the computer to operate as the parameter sequence synthesis device according to any one of claims 9 to 13. A lip-sync animation creation system for creating the face animation synchronized with voice based on a predetermined face object in which a face shape is defined using coordinate values of a plurality of nodes in a first coordinate space. And
A parameter sequence synthesizer according to any one of claims 9 to 13,
By changing the coordinate value of the node in the face object based on a parameter series representing a trajectory of a plurality of feature points of a speaker's face synthesized by the parameter series synthesis device with respect to the voice input , Deformed object generating means for generating an object defining the shape of the face for each frame of the animation;
A lip-sync animation creating system, comprising: an imaging unit for synthesizing the face image in the frame from the object generated by the deformed object generating unit for each frame of the animation.

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