WO2002009040A1 - Method and system for generating an avatar animation transform using a neutral face image - Google Patents

Method and system for generating an avatar animation transform using a neutral face image Download PDF

Info

Publication number
WO2002009040A1
WO2002009040A1 PCT/US2001/041397 US0141397W WO0209040A1 WO 2002009040 A1 WO2002009040 A1 WO 2002009040A1 US 0141397 W US0141397 W US 0141397W WO 0209040 A1 WO0209040 A1 WO 0209040A1
Authority
WO
WIPO (PCT)
Prior art keywords
avatar
generating
head image
animation transform
transform
Prior art date
Application number
PCT/US2001/041397
Other languages
French (fr)
Inventor
Luciano Pasquale Agostino Nocera
Hartmut Neven
Original Assignee
Eyematic Interfaces, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Eyematic Interfaces, Inc. filed Critical Eyematic Interfaces, Inc.
Priority to JP2002514667A priority Critical patent/JP2004509391A/en
Priority to AU2001281335A priority patent/AU2001281335A1/en
Priority to KR10-2003-7001108A priority patent/KR20030029638A/en
Priority to EP01959816A priority patent/EP1436781A1/en
Publication of WO2002009040A1 publication Critical patent/WO2002009040A1/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T13/00—Animation
    • G06T13/20—3D [Three Dimensional] animation
    • G06T13/40—3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

Definitions

  • the present invention relates to avatar animation, and more particularly, to generation of an animation transform using a neutral face image.
  • Virtual spaces filled with avatars are an attractive the way to allow for the experience of a shared environment.
  • manual creation of a photo- realistic avatar is time consuming and automated avatar creation is prone to artifacts and feature distortion.
  • the present invention is embodied in a method, and related system, for generating an avatar animation transform using a neutral face image.
  • the method may include providing a neutral-face front head image and a side head image for generating an avatar and automatically finding head feature locations on the front head image and the side head image using elastic bunch graph matching. Nodes are automatically positioned at feature locations on the front head image and the side head image. The node positions are manually reviewed and corrected to remove artifacts and minimize distorted features in the avatar generated based on the node positions.
  • the method may further include generating an animation transform based on the corrected node positions for the neutral face.
  • the method also may include applying the animation transform to expression face avatar meshes for generating the avatar.
  • FIG. 1 is a flow diagram for illustrating a method for generating an avatar animation transform using a neutral face image, according to the present invention.
  • FIG. 2 is an image of an avatar editor for generating an avatar, according to the present invention.
  • FIG. 3 is an image of a rear view of an avatar generated using anchor points provided by the avatar editor of FIG. 2.
  • FIG. 4 is an image of an avatar editor for generating an avatar using anchor point positions corrected to remove artifacts and distortions from the avatar image, according to the present invention.
  • FIG. 5 is an image of a rear view of an avatar generated using the corrected anchor point positions shown in FIG. 4, according to the present invention.
  • FIG. 6 is a graph of facial expression features versus avatar mesh for linear regression mapping of sensed facial features to an avatar mesh.
  • the present invention is embodied in a method, shown in FIG. 1 , and a system for generating an animation transform using a neutral face image.
  • An avatar editor uses a frontal head image and a side head image of a neutral face model for generating an avatar (block 12).
  • the avatar is generated by automatically finding head feature locations on the front and side head images using elastic bunch graph matching (block 14). Locating features in an image using elastic bunch graph matching is described in U.S. patent application serial number 09/188,079.
  • an image is transformed into Gabor space using a wavelet transformations based on Gabor wavelets.
  • the transformed image is represented by complex wavelet component values associated with each pixel of the original image.
  • Elastic bunch graph matching automatically places node graphs having anchor points on .the front and side head images, respectively.
  • the anchor points are placed at the general location of facial features found using the matching process (block 16).
  • An avatar editor window 26, shown in FIG. 2, allows a user to generate an avatar that looks and appears similar to a model.
  • a new avatar 28 is generated based on the front head image 30 and a side head image 32 of the model.
  • an existing avatar may be edited to the satisfaction of the user.
  • the front and side images are mapped onto an avatar mesh.
  • the avatar may be animated or driven by moving drive control points on the mesh. The motion of the drive control points may be directed by facial feature tracking.
  • the avatar editor window 26 includes a wizard (not shown) that leads the user through a sequence of steps for allowing the user to improve the accuracy of tracking of an avatar tracker.
  • the avatar wizard may include a tutor face that prompts the user to make a number of expressions and varying head poses. An image is taken for each expression or pose and facial features are automatically located for each face image. However, certain artifacts of the image may cause the feature process to place feature nodes at erroneous locations. In addition, correct node locations may generate artifacts that detract from a photorealistic avatar. Accordingly, the user has the opportunity to manually correct the positions of the automatically located features (block 18).
  • the front and side head images, 30 and 32, shown in FIG. 2 have a shadow outline that is erroneously detected as the profile outline of the side head image 32.
  • certain features, such as the model's ears have numerous patterns which may cause erroneous node placement.
  • the avatar 28 may have artificial eye and teeth inserts that are "exposed" while the eyes and/or the mouth are open. Accordingly, although the matching process is able to correctly locate the nodes, of the resulting avatar may have distracting features.
  • Empirical adjustment of the node locations may result in a more photo- realistic avatar.
  • a rear view of the avatar 28, shown in FIG. 3 is generated using the node locations shown in the avatar editor window 26 of FIG.
  • a particularly distracting artifact is a white patch 34 on the rear of the head.
  • the white patch appears because the automatically placed node locations cause a portion of the white background of the side head image 32 to be patched onto the rear of the avatar.
  • the incorrectly placed nodes may be manually adjusted, at shown in FIG. 4, for more accurate placement of the nodes to the corresponding features.
  • Generic head models, 36 and 38 have the node locations indicated so that a user may correctly place the node locations on the front and side head images.
  • a node is moved by clicking a pointer, such as a mouse, on the node and dragging the node to the desired position.
  • the avatar based on the corrected node positions is a more photo-realistic avatar.
  • the node locations at the back of the head on the side head image are adjusted to eliminate the distracting white patch as shown in FIG. 5.
  • the model images shown in FIGS. 2-5 are of a neutral face.
  • Using several avatar meshes corresponding to a variety of facial expressions allows for more accurate depiction of a sensed facial expressions.
  • Meshes for different expressions may be referred to as morph targets.
  • one avatar mesh M SMILE may be generated using features f SMILE from smiling face images.
  • Another avatar mesh M EXCL may be generated using a facial features f EXCL from face images showing surprise or exclamation.
  • the neutral facial features f NEUTRAL correspond the avatar mesh M NEUTRAL .
  • Sensed facial features f SENSED maybe mapped to a corresponding avatar mesh M SENSED using linear regression.
  • a photo-realistic avatar may require as many as 14 to 18 expression-based avatars meshes.
  • the animation transform for the neutral face features may be applied to the other facial expression avatar meshes to improve the quality of the resulting avatars (block 22).
  • the avatar mesh associated with a smile may be transformed by the neutral face animation transform p as indicated in equation 2.
  • M SMILE P • S ILE Equation 2
  • the neutral face-based animation transform provides significant improvement to the facial expression head models without the significant editing time incurred by generating a particular animation transform for each particular facial expression (and/or pose).

Abstract

The present invention is embodied in a method and system for generating an animation transform using a neutral face image. An avatar editor uses a front head image and a side head image of a neutral face model for generating an avatar. The avatar is generated by automatically finding head feature locations on the front and side head images using elastic bunch graph matching. Significant time savings may be accomplished by generating an animation transform relating an avatar mesh derived from the neutral face features to a generic avatar mesh. The animation transform for the neutral face features may be applied to the other facial expression avatar meshes to improve the quality of the resulting avatar. The neutral-face-based animation transform provides significant improvement to the facial expression head models without the significant editing time incurred by generating a particular animation transform for each particular facial expression (and/or pose) features.

Description

METHOD AND SYSTEM FOR GENERATING AN AVATAR ANIMATION TRANSFORM USE G A NEUTRAL FACE IMAGE
CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority under 35 U.S.C. §119(e)(1) and 37 C.F.R.
§ 1.78(a)(4) to U.S. provisional application serial number 60/220,330, entitled METHOD AND SYSTEM FOR GENERATING AN AVATAR ANIMATION TRANSFORM USING A NEUTRAL FACE IMAGE and filed July 24, 2000; and claims priority under 35 U.S.C. § 120 and 37 C.F.R. § 1.78(a)(2) as a continuation-in-part to U.S. patent application serial number 09/188,079, entitled WAVELET-BASED FACIAL MOTION CAPTURE FOR AVATAR ANIMATION and filed November 6, 1998. The entire disclosure of U.S. patent application serial number 09/188,079 is incorporated herein by reference.
BACKGROUND OF THE INVENTION
The present invention relates to avatar animation, and more particularly, to generation of an animation transform using a neutral face image.
Virtual spaces filled with avatars are an attractive the way to allow for the experience of a shared environment. However, manual creation of a photo- realistic avatar is time consuming and automated avatar creation is prone to artifacts and feature distortion.
Accordingly, there exists a significant need for an avatar editor for quickly and reliably generating an avatar head model. The present invention satisfies this need.
SUMMARY OF THE INVENTION
The present invention is embodied in a method, and related system, for generating an avatar animation transform using a neutral face image. The method may include providing a neutral-face front head image and a side head image for generating an avatar and automatically finding head feature locations on the front head image and the side head image using elastic bunch graph matching. Nodes are automatically positioned at feature locations on the front head image and the side head image. The node positions are manually reviewed and corrected to remove artifacts and minimize distorted features in the avatar generated based on the node positions.
The method may further include generating an animation transform based on the corrected node positions for the neutral face. The method also may include applying the animation transform to expression face avatar meshes for generating the avatar.
Other features and advantages of the present invention should be apparent from the following description of the preferred embodiments taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flow diagram for illustrating a method for generating an avatar animation transform using a neutral face image, according to the present invention.
FIG. 2 is an image of an avatar editor for generating an avatar, according to the present invention.
FIG. 3 is an image of a rear view of an avatar generated using anchor points provided by the avatar editor of FIG. 2.
FIG. 4 is an image of an avatar editor for generating an avatar using anchor point positions corrected to remove artifacts and distortions from the avatar image, according to the present invention.
FIG. 5 is an image of a rear view of an avatar generated using the corrected anchor point positions shown in FIG. 4, according to the present invention. FIG. 6 is a graph of facial expression features versus avatar mesh for linear regression mapping of sensed facial features to an avatar mesh.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention is embodied in a method, shown in FIG. 1 , and a system for generating an animation transform using a neutral face image. An avatar editor uses a frontal head image and a side head image of a neutral face model for generating an avatar (block 12). The avatar is generated by automatically finding head feature locations on the front and side head images using elastic bunch graph matching (block 14). Locating features in an image using elastic bunch graph matching is described in U.S. patent application serial number 09/188,079. In the elastic graph matching technique, an image is transformed into Gabor space using a wavelet transformations based on Gabor wavelets. The transformed image is represented by complex wavelet component values associated with each pixel of the original image. Elastic bunch graph matching automatically places node graphs having anchor points on .the front and side head images, respectively. The anchor points are placed at the general location of facial features found using the matching process (block 16).
An avatar editor window 26, shown in FIG. 2, allows a user to generate an avatar that looks and appears similar to a model. A new avatar 28 is generated based on the front head image 30 and a side head image 32 of the model. Alternatively, an existing avatar may be edited to the satisfaction of the user. The front and side images are mapped onto an avatar mesh. The avatar may be animated or driven by moving drive control points on the mesh. The motion of the drive control points may be directed by facial feature tracking.
Initially, the avatar editor window 26 includes a wizard (not shown) that leads the user through a sequence of steps for allowing the user to improve the accuracy of tracking of an avatar tracker. The avatar wizard may include a tutor face that prompts the user to make a number of expressions and varying head poses. An image is taken for each expression or pose and facial features are automatically located for each face image. However, certain artifacts of the image may cause the feature process to place feature nodes at erroneous locations. In addition, correct node locations may generate artifacts that detract from a photorealistic avatar. Accordingly, the user has the opportunity to manually correct the positions of the automatically located features (block 18).
For example, the front and side head images, 30 and 32, shown in FIG. 2 have a shadow outline that is erroneously detected as the profile outline of the side head image 32. Also certain features, such as the model's ears, have numerous patterns which may cause erroneous node placement. Of particular importance is proper placement of the nodes for the eyes and for the mouth. The avatar 28 may have artificial eye and teeth inserts that are "exposed" while the eyes and/or the mouth are open. Accordingly, although the matching process is able to correctly locate the nodes, of the resulting avatar may have distracting features.
Empirical adjustment of the node locations may result in a more photo- realistic avatar. As an example, a rear view of the avatar 28, shown in FIG. 3, is generated using the node locations shown in the avatar editor window 26 of FIG.
2. A particularly distracting artifact is a white patch 34 on the rear of the head.
The white patch appears because the automatically placed node locations cause a portion of the white background of the side head image 32 to be patched onto the rear of the avatar.
The incorrectly placed nodes may be manually adjusted, at shown in FIG. 4, for more accurate placement of the nodes to the corresponding features. Generic head models, 36 and 38, have the node locations indicated so that a user may correctly place the node locations on the front and side head images. A node is moved by clicking a pointer, such as a mouse, on the node and dragging the node to the desired position. As seen by the front view of the avatar 28', the avatar based on the corrected node positions is a more photo-realistic avatar. Further, the node locations at the back of the head on the side head image are adjusted to eliminate the distracting white patch as shown in FIG. 5. The model images shown in FIGS. 2-5 are of a neutral face. As discussed above, images for a variety of facial expressions and poses are captured using training facial expressions. As shown in FIG. 6, facial expression features f are sensed and the resulting parameters may be mapped to corresponding avatar meshes M by a transform T ( M = T (f )). Using several avatar meshes corresponding to a variety of facial expressions allows for more accurate depiction of a sensed facial expressions. Meshes for different expressions may be referred to as morph targets. For example, one avatar mesh MSMILE may be generated using features fSMILE from smiling face images. Another avatar mesh MEXCL may be generated using a facial features fEXCL from face images showing surprise or exclamation. Likewise, the neutral facial features fNEUTRAL correspond the avatar mesh MNEUTRAL . Sensed facial features fSENSED maybe mapped to a corresponding avatar mesh MSENSED using linear regression.
For a more photo-realistic effect, the node positions for each expression should be manually reviewed and artifacts and distortions addressed for each can model. However, empirical experience has shown that correction for each avatar head model may take several minutes of editing time. A photo-realistic avatar may require as many as 14 to 18 expression-based avatars meshes.
Significant time savings may be accomplished by a generating an animation transform p using the neutral face features fNEUTRAL (block 20 - FIG. 1). The resulting avatar mesh M^EUTRAL is related to a generic avatar mesh M NEUTRAL by the avatar transform as indicated in equation 1.
MNEUTRAL = P • M EUTRAL Equation 1
The animation transform for the neutral face features may be applied to the other facial expression avatar meshes to improve the quality of the resulting avatars (block 22). For example, the avatar mesh associated with a smile may be transformed by the neutral face animation transform p as indicated in equation 2. M SMILE = P • S ILE Equation 2
The neutral face-based animation transform provides significant improvement to the facial expression head models without the significant editing time incurred by generating a particular animation transform for each particular facial expression (and/or pose).
Although the foregoing discloses the preferred embodiments of the present invention, it is understood that those skilled in the art may make various changes to the preferred embodiments without departing from the scope of the invention. The invention is defined only by the following claims.
WE CLAIM:

Claims

1. A method for generating an avatar animation transform, comprising: providing a neutral-face front head image and a side head image for generating an avatar; automatically finding head feature locations on the front head image and the side head image using elastic bunch graph matching; automatically positioning nodes at feature locations on the front head image and the side head image; and manually reviewing and correcting the node positions to remove artifacts and minimize distorted features in the avatar generated based on the node positions.
2. A method for generating an avatar animation transform as defined in claim 1, further comprising generating an animation transform based on the corrected node positions for the neutral face.
3. A method for generating an avatar animation transform as defined in claim 2, further comprising applying the animation transform to expression face avatar meshes for generating the avatar.
4. A method for generating an avatar animation transform as defined in claim 2, further comprising applying the animation transform to morph targets.
5. A system for generating an avatar animation transform, comprising: means for providing a neutral-face front head image and a side head image for generating an avatar; means for automatically finding head feature locations on the front head image and the side head image using elastic bunch graph matching; means for automatically positioning nodes at feature locations on the front head image and the side head image; and means for manually reviewing and correcting the node positions to remove artifacts and minimize distorted features in the avatar generated based on the node positions.
6. A system for generating an avatar animation transform as defined in claim 5, further comprising means for generating an animation transform based on the corrected node positions for the neutral face.
7. A system for generating an avatar animation transform as defined in claim 6, further comprising means for applying the animation transform to expression face avatar meshes for generating the avatar.
8. A system for generating an avatar animation transform as defined in claim 6, further comprising means for applying the animation transform to morph targets.
9. A method for generating an avatar animation transform, comprising: providing a neutral-face front head image and a side head image for generating an avatar; automatically finding head feature locations on the front head image and the side head image using image analysis based on wavelet component values generated from wavelet transformations of the respective neutral-face front head image and the side head image; automatically positioning nodes at feature locations on the front head image and the side head image; and manually reviewing and correcting the node positions to remove artifacts and minimize distorted features in the avatar generated based on the node positions.
10. A method for generating an avatar animation transform as defined in claim 9, further comprising generating an animation transform based on the corrected node positions for the neutral face.
11. A method for generating an avatar animation transform as defined in claim 10, further comprising applying the animation transform to expression face avatar meshes for generating the avatar.
12. A method for generating an avatar animation transform as defined in claim 10, further comprising applying the animation transform to morph targets.
13. A method for generating an avatar animation transform as defined in claim 9, wherein the wavelet transformations use Gabor wavelets.
PCT/US2001/041397 2000-07-24 2001-07-24 Method and system for generating an avatar animation transform using a neutral face image WO2002009040A1 (en)

Priority Applications (4)

Application Number Priority Date Filing Date Title
JP2002514667A JP2004509391A (en) 2000-07-24 2001-07-24 Avatar video conversion method and device using expressionless facial image
AU2001281335A AU2001281335A1 (en) 2000-07-24 2001-07-24 Method and system for generating an avatar animation transform using a neutral face image
KR10-2003-7001108A KR20030029638A (en) 2000-07-24 2001-07-24 Method and system for generating an avatar animation transform using a neutral face image
EP01959816A EP1436781A1 (en) 2000-07-24 2001-07-24 Method and system for generating an avatar animation transform using a neutral face image

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
US22033000P 2000-07-24 2000-07-24

Publications (1)

Publication Number Publication Date
WO2002009040A1 true WO2002009040A1 (en) 2002-01-31

Family

ID=22823129

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2001/041397 WO2002009040A1 (en) 2000-07-24 2001-07-24 Method and system for generating an avatar animation transform using a neutral face image

Country Status (5)

Country Link
EP (1) EP1436781A1 (en)
JP (1) JP2004509391A (en)
KR (1) KR20030029638A (en)
AU (1) AU2001281335A1 (en)
WO (1) WO2002009040A1 (en)

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007059077A2 (en) 2005-11-14 2007-05-24 E.I.Du Pont De Nemours And Company Compositions and methods for altering alpha- and beta-tocotrienol content
WO2008147935A2 (en) 2007-05-24 2008-12-04 E. I. Du Pont De Nemours And Company Dgat genes from yarrowia lipolytica for increased seed storage lipid production and altered fatty acid profiles in soybean
WO2011062748A1 (en) 2009-11-23 2011-05-26 E.I. Du Pont De Nemours And Company Sucrose transporter genes for increasing plant seed lipids
EP2620500A2 (en) 2008-05-23 2013-07-31 E. I. du Pont de Nemours and Company DGAT genes from oleaginous organisms for increased seed storage lipid production and altered fatty acid profiles in oilseed plants
WO2016101131A1 (en) * 2014-12-23 2016-06-30 Intel Corporation Augmented facial animation
US9799133B2 (en) 2014-12-23 2017-10-24 Intel Corporation Facial gesture driven animation of non-facial features
US9824502B2 (en) 2014-12-23 2017-11-21 Intel Corporation Sketch selection for rendering 3D model avatar
WO2019177870A1 (en) * 2018-03-15 2019-09-19 Magic Leap, Inc. Animating virtual avatar facial movements
US11069115B2 (en) 2019-02-20 2021-07-20 Samsung Electronics Co., Ltd. Method of controlling display of avatar and electronic device therefor
US11303850B2 (en) 2012-04-09 2022-04-12 Intel Corporation Communication using interactive avatars
US11887231B2 (en) 2015-12-18 2024-01-30 Tahoe Research, Ltd. Avatar animation system

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102565755B1 (en) 2018-02-23 2023-08-11 삼성전자주식회사 Electronic device for displaying an avatar performed a motion according to a movement of a feature point of a face and method of operating the same

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998001830A1 (en) * 1996-07-05 1998-01-15 British Telecommunications Public Limited Company Image processing
WO1999053443A1 (en) * 1998-04-13 1999-10-21 Eyematic Interfaces, Inc. Wavelet-based facial motion capture for avatar animation

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998001830A1 (en) * 1996-07-05 1998-01-15 British Telecommunications Public Limited Company Image processing
WO1999053443A1 (en) * 1998-04-13 1999-10-21 Eyematic Interfaces, Inc. Wavelet-based facial motion capture for avatar animation

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
LEE Y ET AL: "CONSTRUCTING PHYSICS-BASED FACIAL MODELS OF INDIVIDUALS", PROCEEDINGS/COMPTE RENDU GRAPHICS INTERFACE, XX, XX, 1993, pages 1 - 8, XP002064612 *
YUENCHENG LEE ET AL: "REALISTIC MODELING FOR FACIAL ANIMATION", COMPUTER GRAPHICS PROCEEDINGS. LOS ANGELES, AUG. 6 - 11, 1995, COMPUTER GRAPHICS PROCEEDINGS (SIGGRAPH), NEW YORK, IEEE, US, 6 August 1995 (1995-08-06), pages 55 - 62, XP000546216, ISBN: 0-89791-701-4 *

Cited By (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2007059077A2 (en) 2005-11-14 2007-05-24 E.I.Du Pont De Nemours And Company Compositions and methods for altering alpha- and beta-tocotrienol content
WO2008147935A2 (en) 2007-05-24 2008-12-04 E. I. Du Pont De Nemours And Company Dgat genes from yarrowia lipolytica for increased seed storage lipid production and altered fatty acid profiles in soybean
EP2730656A1 (en) 2007-05-24 2014-05-14 E. I. du Pont de Nemours and Company Soybean meal from beans having DGAT genes from yarrowia lipolytica for increased seed storage lipid production and altered fatty acid profiles in soybean
EP2620500A2 (en) 2008-05-23 2013-07-31 E. I. du Pont de Nemours and Company DGAT genes from oleaginous organisms for increased seed storage lipid production and altered fatty acid profiles in oilseed plants
EP2620502A2 (en) 2008-05-23 2013-07-31 E. I. du Pont de Nemours and Company DGAT genes from oleaginous organisms for increased seed storage lipid production and altered fatty acid profiles in oilseed plants
EP2620501A2 (en) 2008-05-23 2013-07-31 E. I. du Pont de Nemours and Company DGAT genes from oleaginous organisms for increased seed storage lipid production and altered fatty acid profiles in oilseed plants
WO2011062748A1 (en) 2009-11-23 2011-05-26 E.I. Du Pont De Nemours And Company Sucrose transporter genes for increasing plant seed lipids
US11303850B2 (en) 2012-04-09 2022-04-12 Intel Corporation Communication using interactive avatars
US11595617B2 (en) 2012-04-09 2023-02-28 Intel Corporation Communication using interactive avatars
US9799133B2 (en) 2014-12-23 2017-10-24 Intel Corporation Facial gesture driven animation of non-facial features
US9830728B2 (en) 2014-12-23 2017-11-28 Intel Corporation Augmented facial animation
US10540800B2 (en) 2014-12-23 2020-01-21 Intel Corporation Facial gesture driven animation of non-facial features
US11295502B2 (en) 2014-12-23 2022-04-05 Intel Corporation Augmented facial animation
US9824502B2 (en) 2014-12-23 2017-11-21 Intel Corporation Sketch selection for rendering 3D model avatar
WO2016101131A1 (en) * 2014-12-23 2016-06-30 Intel Corporation Augmented facial animation
US11887231B2 (en) 2015-12-18 2024-01-30 Tahoe Research, Ltd. Avatar animation system
WO2019177870A1 (en) * 2018-03-15 2019-09-19 Magic Leap, Inc. Animating virtual avatar facial movements
US11430169B2 (en) 2018-03-15 2022-08-30 Magic Leap, Inc. Animating virtual avatar facial movements
US11069115B2 (en) 2019-02-20 2021-07-20 Samsung Electronics Co., Ltd. Method of controlling display of avatar and electronic device therefor

Also Published As

Publication number Publication date
KR20030029638A (en) 2003-04-14
EP1436781A1 (en) 2004-07-14
AU2001281335A1 (en) 2002-02-05
JP2004509391A (en) 2004-03-25

Similar Documents

Publication Publication Date Title
US20020067362A1 (en) Method and system generating an avatar animation transform using a neutral face image
US9978175B2 (en) Real time concurrent design of shape, texture, and motion for 3D character animation
US11517272B2 (en) Simulated orthodontic treatment via augmented visualization in real-time
US20200226821A1 (en) Systems and Methods for Automating the Personalization of Blendshape Rigs Based on Performance Capture Data
Guenter et al. Making faces
US7116330B2 (en) Approximating motion using a three-dimensional model
US9305387B2 (en) Real time generation of animation-ready 3D character models
US6714661B2 (en) Method and system for customizing facial feature tracking using precise landmark finding on a neutral face image
EP1436781A1 (en) Method and system for generating an avatar animation transform using a neutral face image
US20080024505A1 (en) Facs solving in motion capture
US6020892A (en) Process for producing and controlling animated facial representations
US7567251B2 (en) Techniques for creating facial animation using a face mesh
EP2718903B1 (en) Controlling objects in a virtual environment
WO2010060113A1 (en) Real time generation of animation-ready 3d character models
US11393149B2 (en) Generating an animation rig for use in animating a computer-generated character based on facial scans of an actor and a muscle model
US7961947B2 (en) FACS cleaning in motion capture
CN110827397B (en) Texture fusion method for real-time three-dimensional reconstruction of RGB-D camera
EP2615583B1 (en) Method and arrangement for 3D model morphing
CN107230250B (en) Forming method for direct three-dimensional modeling by referring to solid specimen
US11410370B1 (en) Systems and methods for computer animation of an artificial character using facial poses from a live actor
AU2001277148B2 (en) Method and system for customizing facial feature tracking using precise landmark finding on a neutral face image
US20230154094A1 (en) Systems and Methods for Computer Animation of an Artificial Character Using Facial Poses From a Live Actor
JP2022152058A (en) Information processing system, information processing method, and information processing program
JP2002157605A (en) Device and method for image processing, and recording medium with recorded program for image processing
AU2001277148A1 (en) Method and system for customizing facial feature tracking using precise landmark finding on a neutral face image

Legal Events

Date Code Title Description
AK Designated states

Kind code of ref document: A1

Designated state(s): AE AG AL AM AT AU AZ BA BB BG BR BY BZ CA CH CN CO CR CU CZ DE DK DM DZ EC EE ES FI GB GD GE GH GM HR HU ID IL IN IS JP KE KG KP KR KZ LC LK LR LS LT LU LV MA MD MG MK MN MW MX MZ NO NZ PL PT RO RU SD SE SG SI SK SL TJ TM TR TT TZ UA UG UZ VN YU ZA ZW

AL Designated countries for regional patents

Kind code of ref document: A1

Designated state(s): GH GM KE LS MW MZ SD SL SZ TZ UG ZW AM AZ BY KG KZ MD RU TJ TM AT BE CH CY DE DK ES FI FR GB GR IE IT LU MC NL PT SE TR BF BJ CF CG CI CM GA GN GQ GW ML MR NE SN TD TG

121 Ep: the epo has been informed by wipo that ep was designated in this application
DFPE Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101)
REG Reference to national code

Ref country code: DE

Ref legal event code: 8642

WWE Wipo information: entry into national phase

Ref document number: 2001281335

Country of ref document: AU

WWE Wipo information: entry into national phase

Ref document number: 1020037001108

Country of ref document: KR

WWE Wipo information: entry into national phase

Ref document number: 2001959816

Country of ref document: EP

WWP Wipo information: published in national office

Ref document number: 1020037001108

Country of ref document: KR

WWP Wipo information: published in national office

Ref document number: 2001959816

Country of ref document: EP

WWW Wipo information: withdrawn in national office

Ref document number: 2001959816

Country of ref document: EP

DPE2 Request for preliminary examination filed before expiration of 19th month from priority date (pct application filed from 20040101)