Rapid computer modeling of faces for animation

• US 20050207623A1
• Filed: 05/02/2005
• Published: 09/22/2005
• Est. Priority Date: 03/09/2000
• Status: Active Grant

• Current Assignee: Zhigu Holdings Limited
• Original Assignee: Microsoft Corporation
• Inventors: Zhang, Zhengyou, Liu, Zicheng, Cohen, Michael F., Jacobs, Charles E.
• Granted Patent: US 7,158,658 B2
• Time in Patent Office: Days
• US Class Current: 382/118
• CPC Class Codes: G06T 13/40 of characters, e.g. humans, animals or virtual beings
• CPC Class Codes: G06T 15/205 Image-based rendering
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects
• CPC Class Codes: G06T 17/10 Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes
• CPC Class Codes: G06T 17/20 Finite element generation, e.g. wire-frame surface description, tesselation
• CPC Class Codes: G06T 2200/08 involving all processing steps from image acquisition to 3D model generation
• CPC Class Codes: G06T 2207/10012 Stereo images
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10021 Stereoscopic video; Stereoscopic image sequence
• CPC Class Codes: G06T 2207/30201 Face
• CPC Class Codes: G06T 7/251 involving models
• CPC Class Codes: G06T 7/55 from multiple images
• CPC Class Codes: G06T 7/579 from motion
• CPC Class Codes: G06T 7/74 involving reference images or patches
• CPC Class Codes: G06T 9/001 Model-based coding, e.g. wire frame
• CPC Class Codes: G06V 20/64 Three-dimensional objects
• CPC Class Codes: G06V 40/165 using facial parts and geometric relationships
• CPC Class Codes: G06V 40/168 Feature extraction; Face representation
• CPC Class Codes: G06V 40/171 Local features and components; Facial parts eye characteristics G06V40/18; Occluding parts, e.g. glasses; Geometrical relationships
• CPC Class Codes: H04N 19/162 User input
• CPC Class Codes: H04N 19/503 : involving temporal prediction adaptive coding with adaptive selection between spatial and temporal predictive coding H04N19/107; adaptive coding with adaptive selection among a plurality of temporal predictive coding modes H04N19/109

Abstract

Described herein is a technique for creating a 3D face model using images obtained from an inexpensive camera associated with a general-purpose computer. Two still images of the user are captured, and two video sequences. The user is asked to identify five facial features, which are used to calculate a mask and to perform fitting operations. Based on a comparison of the still images, deformation vectors are applied to a neutral face model to create the 3D model. The video sequences are used to create a texture map. The process of creating the texture map references the previously obtained 3D model to determine poses of the sequential video images.

Claims

1. One or more computer-readable media containing instructions to process two or more images for distinguishing a head in the images from other portions of the images, the instructions being executable to perform actions comprising:

computing a first mask image that marks any image pixels whose colors are different in the two or more images;

creating a second mask image that marks any image pixels having skin colors; and

combining the first and second mask images to create a final mask image.

2. One or more computer-readable media as recited in claim 1, the actions further comprising:

calculating the skin colors by sampling the images.

3. One or more computer-readable media as recited in claim 1, the actions further comprising:

calculating one or more skin colors by sampling the images at portions corresponding to facial features.

4. One or more computer-readable media as recited in claim 1, wherein the combining comprises intersecting.

5. One or more computer-readable media as recited in claim 1, wherein the combining comprises joining.

6. One or more computer-readable media as recited in claim 1, the actions further comprising:

predicting an outer area that corresponds to the head, based on locations of facial features;

predicting an inner area within the outer area that corresponds to a face portion of the head;

wherein said noting is performed on marked pixels within the inner area.

7. One or more computer-readable media as recited in claim 6, wherein the inner and outer areas are defined by inner and outer ellipses, and the outer ellipse is approximately 25% larger than the inner ellipse.

8. An apparatus configured to process two or more images for distinguishing a head in the images from other portions of the images, the apparatus being configured to perform actions comprising:

computing a first mask image that marks any image pixels whose colors are different in the two or more images;

creating a second mask image that marks any image pixels having skin colors; and

combining the first and second mask images to create a final mask image.

9. An apparatus as recited in claim 8, the actions further comprising:

calculating the skin colors by sampling the images.

10. An apparatus as recited in claim 8, the actions further comprising:

calculating the skin colors by sampling the images at portions corresponding to facial features.

11. An apparatus as recited in claim 8, wherein the combining comprises intersecting.

12. An apparatus as recited in claim 8, wherein the combining comprises joining.

13. An apparatus as recited in claim 8, the actions further comprising:

predicting an outer area that corresponds to the head, based on locations of facial features;

predicting an inner area within the outer area that corresponds to a face portion of the head; and

wherein said noting is performed on marked pixels within the inner area.

14. An apparatus as recited in claim 13, wherein the inner and outer areas are defined by inner and outer ellipses, and the outer ellipse is approximately 25% larger than the inner ellipse.

15. A method to process two or more images for distinguishing a head in the images from other portions of the images, the method comprising:

creating a first mask image that marks any image pixels whose colors are different in the two images;

creating a second mask image that marks any image pixels whose colors correspond to skin colors;

noting marked pixels on the first mask image and also noting unmarked pixels of the first mask image that correspond in location to marked pixels in the second mask image; and

forming a final mask image that marks the noted pixels as being part of the head.

16. A method as recited in claim 15, the actions further comprising:

identifying locations of a plurality of distinct facial features in the images;

predicting an outer area that corresponds to the head, based on the identified locations of facial features;

predicting an inner area within the outer area that corresponds to a face portion of the head, based on the identified locations of facial features; and

calculating the skin colors by sampling the images at locations that are specified relative to the identified locations of facial features.

17. A method as recited in claim 15, the actions further comprising:

predicting a lower area of the image that corresponds to a chin portion of the head; and

within the lower area, noting marked pixels in the first mask image that correspond in location to marked pixels in the second mask image.

18. A method as recited in claim 15, further comprising:

predicting an outer area that corresponds to the head, based on locations of facial features;

predicting an inner area within the outer area that corresponds to a face portion of the head;

wherein said noting is performed on marked pixels within the inner area.

19. A method as recited in claim 18, wherein the inner and outer areas are defined by inner and outer ellipses, and the outer ellipse is approximately 25% larger than the inner ellipse.

20. A method as recited in claim 18, further comprising:

predicting a lower area of the image that corresponds to a chin portion of the head; and

within the lower area, noting marked pixels in the first mask image that correspond in location to marked pixels in the second mask image.