Rapid computer modeling of faces for animation

• US 6,980,671 B2
• Filed: 05/14/2004
• Issued: 12/27/2005
• Est. Priority Date: 03/09/2000
• Status: Expired due to Term

• Current Assignee: Zhigu Holdings Limited
• Original Assignee: Microsoft Corporation
• Inventors: Zhang, Zhengyou, Liu, Zicheng, Cohen, Michael F., Jacobs, Charles E.
• Primary Examiner(s): Boudreau, Leo
• Assistant Examiner(s): SHERALI, ISHRAT I
• Application Number:
  US10/846,086
• Publication Number:
  US 20040213438A1
• Time in Patent Office: 592 Days
• Field of Search: 382115-118, 382/103, 382/107, 382162-165, 382/236, 382/173, 382/190, 348/154, 348/155, 348/169, 348 1408- 141, 348 77- 78, 345/589, 345/419, 345/420, 345/423, 345/473
• 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. A method of processing two or more images to distinguish a head in the images from other portions of the images, comprising the following actions:

predicting portions of the images that form facial portions of the images;

calculating one or more skin colors by sampling the images at the predicted portions;

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 whose colors correspond to the calculated one or more skin colors;

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

2. A method as recited in claim 1, wherein the combining comprises intersecting.

3. A method as recited in claim 1, wherein the combining comprises joining.

4. A method as recited in claim 1, wherein the predicting is based on points supplied by a human user.

5. A method as recited in claim 1, wherein the predicting is based on point supplied by a human user, the points corresponding to a plurality of distinct facial features.

6. One or more computer-readable media containing a program that is executable by a computer to process two or more images for distinguishing a head in the images from other portions of the images, the program comprising the following actions:

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

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

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

calculating one or more skin colors by sampling the images at locations that are specified relative to the indicated locations of facial features;

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 the calculated one or more skin colors;

within the inner area, noting all of the marked pixels on the first mask image and also noting any unmarked pixels of the first mask image that correspond in location to marked pixels in the second mask image;

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

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

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

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

8. One or more computer-readable media as recited in claim 6, wherein the identifying comprises accepting input from a human user.

9. One or more computer-readable media as recited in claim 6, wherein the identified locations correspond to eyes, nose and mouth.

10. One or more computer-readable media as recited in claim 6, wherein the identified locations comprise eye corners, mouth ends, and nose tip.

11. 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.

12. One or more computer-readable media as recited in claim 6, wherein:

the sizes of the ellipses are calculated based on spacial relationships of the identified locations.

13. One or more computer-readable media as recited in claim 6, wherein:

the identified locations comprise the inner eye corners and the mouth ends;

the inner and outer areas are defined by inner and outer ellipses; and

the sizes of the ellipses are calculated based on spacial relationships of the identified locations.

14. One or more computer-readable media as recited in claim 6, wherein:

the identified locations comprise the inner eye corners and the mouth ends;

the inner and outer areas are defined by inner and outer ellipses;

the width of the inner ellipse is five times the distance between the inner eye corners; and

the height of the inner ellipse is three times the vertical distance between the eye corners and the mouth ends.

15. One or more computer-readable media as recited in claim 6, wherein:

the identified locations comprise the inner eye corners and the mouth ends;

the inner and outer areas are defined by inner and outer ellipses;

the width of the inner ellipse is five times the distance between the inner eye corners;

the height of the inner ellipse is three times the vertical distance between the eye corners and the mouth ends; and

the outer ellipse is 25% larger than the inner ellipse.