WO2002030171A2 - Facial animation of a personalized 3-d face model using a control mesh - Google Patents

Facial animation of a personalized 3-d face model using a control mesh Download PDF

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Publication number
WO2002030171A2
WO2002030171A2 PCT/IB2001/002363 IB0102363W WO0230171A2 WO 2002030171 A2 WO2002030171 A2 WO 2002030171A2 IB 0102363 W IB0102363 W IB 0102363W WO 0230171 A2 WO0230171 A2 WO 0230171A2
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Prior art keywords
mesh
face
images
geometry
locations
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PCT/IB2001/002363
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English (en)
French (fr)
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WO2002030171A3 (en
Inventor
Tanju A. Erdem
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Erdem Tanju A
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Application filed by Erdem Tanju A filed Critical Erdem Tanju A
Priority to EP01986569A priority Critical patent/EP1334470A2/en
Publication of WO2002030171A2 publication Critical patent/WO2002030171A2/en
Publication of WO2002030171A3 publication Critical patent/WO2002030171A3/en

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    • 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
    • G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation

Definitions

  • the present invention is related to the field of computer generated modeling, and more specifically, to a technique for generating a personalized three-dimensional (3-D) face model from a sequence of two-dimensional (2-D) images of a person's face.
  • 3-D face model of a person involves mapping a real image of the face of the person onto a 3-D triangular mesh that has been shaped to have the same or similar geometry as the face of the person.
  • 3-D triangular mesh refers to a connected set of triangular patches in 3-D whose corners form the nodes of the mesh. Each triangular patch in the 3-D mesh acquires its image data from an associated triangular region in the image of the face.
  • the methods disclosed in the prior art for generating a 3-D face model can be generally classified as those that involve (i) a fully manual process, (ii) a semi-automatic process and (iii) a fully-automatic process.
  • a fully manual process every triangular patch of the 3-D mesh has to be manually mapped onto the image of the face according to the facial features of the face.
  • Fully manual processes are labor intensive and time consuming because the number of triangular patches in the 3-D mesh could range from a hundred to several thousands.
  • Semi-automatic processes rely on automatically detecting or manually marking certain features on the face, such as eyes, nose and mouth, and initialize the 3-D mesh by an affine warping of a standard 3-D mesh based on the location of the detected facial features.
  • a global affine transformation generally does not match the many local facial dimensions.
  • the locations of the nodes are fine-tuned in a manual process for each person.
  • the composite image is a mosaic (or sprite) image of the face that is produced either by a 3-D rotating camera or by stitching a number of 2-D images of the face. While the former process is very costly, the latter one is generally very inaccurate.
  • the present invention proposes a semi- automatic method for generating the 3-D mesh with minimal manual fine tuning.
  • the present invention also proposes a simpler and more general technique for generating the texture image, that involves only concatenating and color blending the 2-D images of the face.
  • the present invention provides an improvement designed to satisfy the aforementioned needs.
  • the present invention is directed to a computer program product for creating a 3-D face model from a plurality of 2-D images of a person's face, by performing the steps of: (a) receiving the plurality of images of a person; (b) obtaining the geometry mesh by deforming a predefined standard 3-D triangular mesh based on the dimensions and relative positions of the person's facial features; and (c) obtaining the texture image by compositing a plurality of 2-D images of the person taken from particular directions and modifying them in boundary regions to achieve seamless stitching of color for the 3-D face model.
  • a computer program product for creating a 3-D face model from a plurality of 2-D images of a person's face, by performing the steps of: (a) receiving the plurality of images of a person; (b) obtaining the geometry mesh by deforming a predefined standard 3-D triangular mesh based on the dimensions and relative positions of the person's facial features; and (c
  • FIG. 1 is a perspective view of a computer system for implementing the present invention
  • FIG. 2 is a diagram illustrating the method of present invention
  • FIG. 3 is a flowchart for the method of the present invention.
  • FIG. 4a is a diagram illustrating the method of calculating the calibration parameter of the camera with a target object
  • FIG. 4b is a diagram illustrating the image of the target object captured by the camera
  • FIG. 5 is a diagram illustrating the method of acquiring a plurality of images of a person's face using the camera
  • FIG. 6 is a diagram further illustrating the method of acquiring a plurality of images of a person's face using the camera
  • FIG. 7 is a diagram illustrating the methods of specifying and locating the feature points of the face
  • FIG. 8 is a first table further illustrating the method of FIG. 7;
  • FIG. 9 is a second table further illustrating the method of FIG. 7;
  • FIG. 10 is a diagram illustrating the method of selecting an initial geometry mesh for the face
  • FIG. 11 is a diagram illustrating the method of making global modifications to the geometry mesh
  • FIG. 12 is a diagram illustrating the method of making local modifications to the geometry mesh
  • FIG. 13 is a diagram further illustrating the method of making local modifications to the geometry mesh
  • FIG. 14 is a diagram further illustrating the method of making local modifications to the geometry mesh
  • FIG. 15 is a diagram illustrating the method of selecting the shade images
  • FIG. 16 is a diagram illustrating the method of blending the shade images. DETAILED DESCRIPTION OF THE INVENTION
  • the computer system 10 includes a microprocessor-based unit 12 for receiving and processing software programs and for performing other well known processing functions.
  • the software programs are contained on a computer useable medium 14, typically a compact disk, and are input into the microprocessor-based unit 12 via the compact disk player 16 electronically connected to the microprocessor-based unit 12.
  • programs could also be contained in an Internet server 18 and input into the microprocessor-based unit 12 via an Internet connection 20.
  • a camera 22 is electronically connected to the microprocessor-based unit 12 to capture the 2-D images of a person's face.
  • a display 24 is electronically connected to the microprocessor-based unit 12 for displaying the images and user related information associated with the software.
  • a keyboard 26 is connected to the microprocessor based unit 12 for allowing a user to input information to the software.
  • a mouse 28 is also connected to the microprocessor based unit 12 for selecting items on the display 24 or for entering 2-D position information to the software, as is well known in the art.
  • a digital pen 30 and a digital pad 32 may be used for selecting items on the display 24 and entering position information to the software.
  • the output of the computer system is either stored on a hard disk 34 connected to the microprocessor unit 12, or uploaded to the Internet server 18 via the Internet connection 20. Alternatively, the output of the computer system can be stored on another computer useable medium 14, typically a compact disk, via a compact disk writer 36.
  • 3-D face model is composed of a 3-D triangular mesh (geometry mesh) 41 and a 2-D composite image (texture image) 42.
  • 3-D triangular mesh refers to a connected set of triangular patches in 3-D whose corners form the nodes of the mesh.
  • Each triangular patch 43 in the geometry mesh 41 is associated with a triangular region 44 in the texture image 42.
  • each triangular patch 43 in the geometry mesh 41 is painted with the image data contained in its corresponding triangle 44 in the texture image 42.
  • Image data are transferred from a triangle 44 in the texture image 42 to its counterpart 43 in the geometry mesh 41 via an affine transform which is well known to anyone knowledgeable in the field of image processing.
  • the seven steps are as follows: (a) calculating the calibration parameter of the camera (Step 110); (b) acquiring a plurality of images of a person's face using the camera (Step 120); (c) calculating the facial dimensions and the position and orientation of the face in the acquired images (Step 130); (d) obtaining a geometry mesh and an associated shape mesh for the face (Step 140); (e) creating a texture image for painting the surface of the deformed geometry mesh (Step 150); (f) adding any synthetic components to the face model (Step 160); (g) storing or transmitting the face model (Step 170).
  • a perspective image of a target object is captured with the camera with the target object being placed at approximately the same distance from the camera as the person's face.
  • the method of the present invention uses the perspective image of the target object to calculate a camera parameter that is used in the subsequent steps, hereinafter referred to as the E parameter.
  • the E parameter has a non-negative value and it is a measure of the amount of perspective deformation caused by the camera. A zero value indicates no perspective deformation and the larger the value of the E parameter the more the perspective deformation caused by the camera.
  • the method of acquiring a plurality of images of a person's face using the camera comprises the steps of (1) acquiring neutral images of the face (Step 121); and (2) acquiring action images of the face (Step 122). In the following, a detailed description of these steps is given.
  • a plurality of 2-D images of the person's face in the same neutral state are captured with the camera from different directions.
  • the neutral state for the face means that all face muscles are relaxed, eyes are normally open, mouth is closed and lips are in contact. These images are subsequently used to obtain the neutral geometry of the face model, hence, hereinafter they are referred to as the neutral images.
  • the camera directions to capture neutral images are selected so that the majority of facial features such as eyes, eyebrows, ears, nose and lips are visible in all images.
  • the face is not required to be at the same distance from the camera in all the neutral images.
  • fifteen camera directions selected for obtaining the neutral images are selected for obtaining the neutral images.
  • the camera remains fixed and the person rotates his/her head to realize the following fifteen different directions: front 221, forehead 222, chin 223, angled-right 224, angled-right-tilted-down 225, angled-right-tilted-up 226, angled-left 227, angled-left- tilted-down 228, angled-left-tilted-up 229, full-right-profile 230, full-right-profile-tilted- down 231, full-right-profile-tilted-up 232, full-left-profile 233, full-left-profile-tilted- down 234, and full-left-profile-tilted-up 235.
  • a plurality of 2-D images of the person's face in action states are captured with the camera from different directions.
  • the action states for the face include faces with a smiling mouth, a yawning mouth, raised eyebrows, etc. These images are subsequently used to obtain the action geometries of the face model, hence, hereinafter they are referred to as the action images.
  • the camera directions to capture the action images are selected so that the majority of facial features such as eyes, eyebrows, ears, nose and lips are visible in all images.
  • the face is not required to be at the same distance from the camera in all the action images.
  • facial action states are as follows: smiling mouth, yawning mouth, kissing mouth, raised eyebrows, and squeezed eyebrows.
  • the camera directions are front and right.
  • the method of calculating the facial dimensions and the position and orientation of the face in the acquired images comprises the steps of (1) specifying feature points of the face (Step 131); (2) locating the feature points on the neutral and action images (Step 132); (3) calculating the 3-D positions of the feature points (Step 133); and (4) calculating the position and orientation of the face in the neutral and action images (Step 134).
  • Step 131 specifying feature points of the face
  • Step 132 locating the feature points on the neutral and action images
  • Step 133 calculating the 3-D positions of the feature points
  • Step 134 calculating the position and orientation of the face in the neutral and action images
  • a plurality of clearly identifiable and sparsely distributed points on the face are selected as the feature points.
  • the following thirteen locations on the person's face are specified as the feature points: the centers of the right 251 and left 252 eye pupils, the central end points of the right 253 and left 254 eyebrows, the right 255 and left 256 corners of the nose, the top 257 and bottom 258 points of the right ear, the top 259 and bottom 260 points of the left ear, the right 261 and left 262 corners of the mouth, and the mid-point 263 of the line where the upper and lower lips contact each other.
  • the feature points are automatically located or manually marked on the acquired images. It is important to note that not all feature points may be visible in all neutral and action images and some feature points are not in their neutral position in some action images. Thus, in the present invention, the location of only the visible feature points and feature points that are in their neutral position are automatically detected or manually marked in each neutral and action image.
  • the feature points are manually marked in the neutral images that are indicated with an X in the table in FIG. 8, and are manually marked in action images that are indicated with an X in FIG. 9.
  • the feature points are assumed as invisible in those neutral images that are not indicated with an X in the table in FIG. 8.
  • the feature points are not in their neutral position in those action images that are not indicated with an X in the table in FIG. 9.
  • the computer program prompts the user to manually mark only the visible feature points and feature points that are in their neutral position in each image.
  • the 3-D positions of the feature points of the person's face are calculated using a modified version of the method in "Shape and Motion from Image Streams under Orthography: A Factorization Method" by Carlo Tomasi and Takeo Kanade, International Journal of Computer Vision, vol. 9, no. 2, pp. 137-154, 1992.
  • a general mathematical analysis of 2-D image projections of 3-D feature points is given.
  • the method of "Shape and Motion from Image Streams under Orthography” is reviewed.
  • the proposed modification to the method of "Factorization of Shape and Motion” is presented.
  • the image plane passes at (0,0,-E) and is perpendicular to k .
  • N denote the number of feature points
  • P n , n ⁇ ,...,N, of all the feature points are changed. It is therefore more appropriate to use a local coordinate system for the face to represent the coordinates of the feature points.
  • the origin C 0 of the local coordinate system is defined to be the centroid of the feature points and is given by
  • W is some constant in units of meters that will be defined shortly.
  • the quantities on the left hand side are measured quantities while the quantities on the right hand side are unknown quantities.
  • the method of "Factorization of Shape and Motion" solves the above equations for the 3-D local coordinates S take of the feature points, and the orientation vectors I f and J f and the 2-D position (c f o, x ,c f o, y ) of the centroid of the feature points in all images in terms of the 2-D projected positions (p f n , x , p f n, y ) of the feature points in all images.
  • the third orientation vector K f is uniquely defined by the first two orientation vectors I f and J f simply as
  • the number of iterations is selected to be 50 and the threshold is selected to be 1 pixel.
  • the method of calculating the 3-D position and orientation of the person's face in the neutral and action images is disclosed in the following. It facilitates understanding to note that the 3-D position of the face in an image is described by the centroid (c f , x ,c f o, y ) of the feature points and the camera-distance-ratio ⁇ / of the face in that image.
  • the 3-D orientation of the face in an image / is described by the vectors P and J / in that image.
  • the 3-D position and orientation parameters (c f o, x ,c f o, y ) , ⁇ f , I f and J f are calculated using the following steps:
  • the number of iterations is selected to be 50 and the threshold is selected to be 1 pixel.
  • the method of obtaining the geometry and shape meshes for the neutral and action faces comprises the steps of (1) selecting and initial geometry mesh for the face (Step 151), (2) making global modifications to the geometry mesh according to the 3-D position data of the feature points (Step 152); (3) making local modifications to the geometry mesh to match the shape of the person's face (Step 153); and (4) defining the shape meshes for the action faces (Step 154).
  • Step 151 selecting and initial geometry mesh for the face
  • Step 152 making global modifications to the geometry mesh according to the 3-D position data of the feature points
  • Step 153 making local modifications to the geometry mesh to match the shape of the person's face
  • Step 154 defining the shape meshes for the action faces
  • a user selects an initial geometry mesh 271 among a collection 272 of standard predefined geometry meshes that best fits the facial type of the person.
  • the facial type of the person includes the skull type, hair type, nose type, and chin type.
  • a user is provided with separate collections of 3-D triangular meshes that represent different skull types, hair types, nose types, and chin types, and is allowed to stitch together a selection from each collection of facial parts to obtain the initial geometry mesh for the person's face.
  • triangular meshes those skilled in the art will understand that any other polygonal mesh could be substituted for the triangular mesh.
  • the globally translated and rotated 3-D positions of the feature points of the person's face obtained in Step 133 are used to globally deform the initial geometry mesh 271 to match the relative positions of the feature points on the globally deformed geometry mesh 273 and to match the global proportions of the person's face.
  • f x and f 2 denote the 3-D positions of the right-eyebrow-central 251 and left-eyebrow-central 252, respectively; and b denote the 3-D position of the lip-
  • R x and R 2 denote the 3-D positions of the geometry-mesh-right-ear- top 265 and geometry-mesh-left-ear-top 266, respectively;
  • F x and F 2 denote the 3-D positions of the geometry-mesh-right-eyebrow-central 267 and geometry-mesh-left- eyebrow-central 268, respectively;
  • b denote the 3-D position of the geometry-mesh- lip-center 269.
  • the vectors u , v, w, U, V , and W are used to globally rotate, scale, and shear the initial geometry mesh 271 to match the global dimensions of the person's face.
  • the process of rotation, scaling, and shear are carried out in that order as explained in the following:
  • Rotation Rotate the initial geometry mesh 271 so that the vector V is aligned with the vector .
  • the geometry mesh 273 obtained in Step 151 is moved and rotated in 3-D and displayed simultaneously with any image using the 3-D motion calculated in Step 134 for that image.
  • Local adjustments are made on the geometry mesh 273 to match the local geometry of the face by moving the nodes of the geometry mesh 273 via a user interface.
  • the nodes of the geometry mesh 273 are moved indirectly using a separate lower resolution (coarser) triangular mesh, hereinafter referred to as the shape mesh 275, overlying the geometry mesh and comprising substantially fewer and larger triangular patches than the geometry mesh.
  • the user moves only the nodes of the shape mesh 275 and the nodes of the geometry mesh 273 move automatically.
  • the shape mesh is selected so that the nodes of the shape mesh are selected from the nodes of the geometry mesh, i.e., a subset of the nodes of the geometry mesh 271 define the shape mesh 275.
  • the shape mesh 275 is a lower resolution mesh than the geometry mesh 271.
  • the feature points defined in Step 131 of the present invention are included in the collection of the nodes of the shape mesh. The method of indirectly moving the nodes of the geometry mesh 273 by moving the nodes of the shape mesh 275 is disclosed in the following.
  • each and every node 280 of the geometry mesh 273 is attached to, and hence controlled by, a triangle 281 of the shape mesh 275, following the global adjustments made to the shape 275 and the geometry mesh 273 in Step 151.
  • the following procedure is used to attach the nodes of the geometry mesh 273 to the triangles of the shape mesh 275:
  • the normal vector n A at the node_4 is obtained by averaging and the surface normals of all the triangles of the shape mesh 275 that have the node A as one of their corners. The result of the averaging is normalized so that the vector n A has unit length.
  • n P an A + ⁇ n B +yn c where the weights ⁇ , ⁇ and ⁇ satisfy 0 ⁇ a, ⁇ , ⁇ 1 and are uniquely determined by solving the equation
  • a node of the geometry triangle is attached to a triangle of the shape mesh 275 only if there is a point on the triangle such that the line passing through the node and the point is parallel to the surface normal vector at the point.
  • the node 280 of the geometry mesh 273 located at Q is attached to the triangle 281 of the shape mesh 275 because the line passing through Q and P is parallel to n p .
  • the node Q 280 is attached to the triangle ABC 281 at the point P .
  • This attachment is quantified by four numbers, namely the weights ⁇ , ⁇ and ⁇ , and the distance d between the node Q 280 and the attachment point P .
  • the nose part of the geometry mesh 273 is adapted to the person's nose.
  • geometry mesh 273 definitions conforming to each action state of the face are also obtained.
  • the nodes of the shape mesh 275 are moved to deform the geometry mesh 273 for each facial action state so that the deformed geometry mesh fits the geometry of the face in the action state.
  • a subset of the neutral and action images are used to obtain the texture image of the face model. These images hereinafter are referred to as the shade images.
  • the texture image is a composite of the shade images.
  • the shade images correspond to special camera directions such as front, right, left, top, and bottom.
  • creating the texture image for painting the surface of the geometry mesh involves the steps of (1) selecting the shade images (Step 171); and (2) blending the shade images (Step 172). In the following, a detailed description of these steps are given.
  • a subset of the neutral and action images are selected as shade images and are used to form the texture image 290 for painting the surface of the geometry mesh 273.
  • the number of shade images is N
  • the triangles of the geometry mesh 273 are divided into N disjoint regions, hereinafter referred to as the texture regions, so that the triangles that are in the same texture region acquire their texture data from the same shade image.
  • Shade images are selected so that the triangles in a texture region are generally more clearly visible in the shade image of the texture region than in any other shade image. It is important to note that the texture regions are selected so that the triangles that are in the same texture region are connected with each other.
  • the polygon that forms the boundary of a texture region is referred to as the boundary polygon for that texture region.
  • the triangles in a texture region that are on the boundary of the texture region are referred to as the boundary triangles for that texture region.
  • the triangles that are on the boundary of a neighboring texture region are referred to as the neighboring boundary triangles for that texture region.
  • the following five neutral images and one action image are selected as the shade images: front 221, front-top 222, front-bottom 223, right-most 230, left-most 233, and yawning-mouth-front 241.
  • the texture image 290 is formed by compositing these shade images. Still referring to FIG. 14, the corresponding texture regions are respectively referred to as front region 291, top region 292, bottom region 293, right region 294, and left region 295.
  • the front-boundary triangle 296 is inside the front region 291 and on the boundary polygon of the front region 291, refe ⁇ ed to as the front-boundary polygon 298.
  • the right-boundary triangle 297 is inside the right region 294 and on the boundary polygon of the right region 294, referred to as the right-boundary polygon 299.
  • the front-boundary triangle 296 and the right boundary triangle 297 are neighboring triangles. Still referring to FIG.
  • the 2-D projection of the front-boundary triangle 296 on the front image 221 is refe ⁇ ed to as the front-projected front-boundary triangle 306 and the 2-D projection of the front-boundary triangle 296 on the full-right-profile image 230 is refe ⁇ ed to as the right-projected-front-boundary triangle 316.
  • the 2-D projection of the right-boundary triangle 297 on the full- right-profile image 230 is refe ⁇ ed to as the right- projected-right-boundary triangle 307 and the 2-D projection of the right-boundary triangle 297 on the front image 221 is refe ⁇ ed to as the front-projected right-boundary triangle 317.
  • the color inside the front-projected-front-boundary triangle 306 is blended with the color inside the right-projected-front-boundary triangle
  • the mouth region is part of the front region 291, and the image data along its boundary is not blended with the image data of any other region.
  • a 3-D mesh model of a pair of eyeglasses is selected from a list of candidate models that best resembles the actual pair of eyeglasses worn by the person.
  • a 3-D mesh model of a pair of earrings is selected from a collection of earrings that best resembles the actual pair of earrings worn by the person.
  • the selected 3-D mesh models are scaled automatically to fit the dimensions of the face of the person and positioned automatically on the ears and the nose regions of the geometry mesh model 273 of the person.
  • the aforementioned components of the face model generated via a computer can be stored on a computer useable medium and/or transmitted over the Internet to another computer.
PCT/IB2001/002363 2000-10-12 2001-10-09 Facial animation of a personalized 3-d face model using a control mesh WO2002030171A2 (en)

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