IP Library Granted Patent US 10,388,078
Granted Patent B2
US 10,388,078 · App. 15/700,686 · Granted Aug 20, 2019

Parameterized model of 2D articulated human shape

Inventors: Michael J. Black (Tuebingen, DE); Oren Freifeld (Menlo Park, CA); Alexander W. Weiss (Shirley, MA); Matthew M. Loper (Tuebingen, DE); Peng Guan (Mountain View, CA)
Assignee: BROWN UNIVERSITY
G06T19/20G06K9/00214G06K9/00369G06K9/48G06K9/6209G06T7/13G06T7/149G06T15/20G06T17/00G06T17/20G06T3/40G06T3/60G06T11/60G06T2207/20081G06T2207/30196G06T2210/16
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 10,388,078
App. No.
15/700,686
Granted
Aug 20, 2019
Kind
B2
Abstract

Disclosed are computer-readable devices, systems and methods for generating a model of a clothed body. The method includes generating a model of an unclothed human body, the model capturing a shape or a pose of the unclothed human body, determining two-dimensional contours associated with the model, and computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body. Based on the two-dimensional contours and the deformations, the method includes generating a first two-dimensional model of the unclothed human body, the first two-dimensional model factoring the deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation and learning an eigen-clothing model using principal component analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing, to yield a second two-dimensional model of a clothed human body.

Claims (35)

1. A method, comprising:

generating a three-dimensional model of an unclothed human body;

generating, based on two-dimensional contours associated with the three-dimensional model, a two-dimensional model of the unclothed human body, the two-dimensional model of the unclothed human body factoring deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation; and

generating a two-dimensional model of a clothed human body based on a learning of an eigen-clothing model using an analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing.

2. The method of claim 1 , wherein the three-dimensional model captures at least one of a shape or a pose of the unclothed human body.

3. The method of claim 1 , further comprising:

computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body, wherein generating the two-dimensional model of the unclothed human body is based at least in part on the deformations.

4. The method of claim 1 , wherein the two-dimensional model of the unclothed human body visualizes frontal views and non-frontal views of the unclothed human body.

5. The method of claim 1 , further comprising:

generating a two-dimensional model based on the two-dimensional contours associated with the three-dimensional model.

6. The method of claim 1 , wherein the pose variation comprises at least one of a body parts rotation and foreshortening.

7. The method of claim 1 , wherein the deformations of the unclothed human body comprise non-rigid deformations resulting from articulation.

8. The method of claim 1 , wherein the two-dimensional model of the unclothed human body is factored into a linear approximation to distortions caused by local camera view changes.

9. The method of claim 1 , wherein the two-dimensional model of the unclothed human body is factored into an articulation of body parts represented by a rotation and length scaling.

10. The method of claim 1 , wherein the two-dimensional model of the unclothed human body is factored into a linear model characterizing shape changes across a population.

11. The method of claim 1 , wherein the two-dimensional model of a clothed human body is further generated by defining a set of linear coefficients that produce, from the unclothed human body, a particular deformation associated with a clothing type.

12. A system comprising:

a processor; and

a non-transitory computer-readable storage medium having stored therein instructions which, when executed by the processor, cause the processor to perform operations comprising:

generating a three-dimensional model of an unclothed human body;

generating, based on two-dimensional contours associated with the three-dimensional model, a two-dimensional model of the unclothed human body, the two-dimensional model of the unclothed human body factoring deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation; and

generating a two-dimensional model of a clothed human body based on a learning of an eigen-clothing model using an analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing.

13. The system of claim 12 , wherein the three-dimensional model captures at least one of a shape or a pose of the unclothed human body.

14. The system of claim 12 , wherein the non-transitory computer-readable storage medium stores therein additional instructions which, when executed by the processor, cause the processor to perform operations further comprising:

computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body, wherein generating the two-dimensional model of the unclothed human body is based at least in part on the deformations.

15. A non-transitory computer-readable storage device having stored therein instructions which, when executed by a processor, cause the processor to perform operations comprising:

generating a three-dimensional model of an unclothed human body;

generating, based on two-dimensional contours associated with the three-dimensional model, a two-dimensional model of the unclothed human body, the two-dimensional model of the unclothed human body factoring deformations of the unclothed human body into one or more of a shape variation component, a viewpoint change, and a pose variation; and

generating a two-dimensional model of a clothed human body based on a learning of an eigen-clothing model using an analysis applied to the deformations, wherein the eigen-clothing model classifies different types of clothing.

16. The non-transitory computer-readable storage device of claim 15 , wherein the non-transitory computer-readable storage device stores additional instructions which, when executed by a processor, cause the processor to perform operations further comprising:

computing deformations by aligning a contour of a clothed human body with a contour of the unclothed human body, wherein generating the two-dimensional model of the unclothed human body is based at least in part on the deformations.

17. The non-transitory computer-readable storage device of claim 15 , wherein the non-transitory computer-readable storage device stores additional instructions which, when executed by a processor, cause the processor to perform operations further comprising: generating a two-dimensional model based on the two-dimensional contours associated with the three-dimensional model.

18. The non-transitory computer-readable storage device of claim 15 , wherein the deformations of the unclothed human body comprise non-rigid deformations resulting from articulation.

19. The non-transitory computer-readable storage device of claim 15 , wherein the two-dimensional model of the unclothed human body is factored into a linear approximation to distortions caused by local camera view changes.

20. The non-transitory computer-readable storage device of claim 15 , wherein the two-dimensional model of the unclothed human body is factored into an articulation of body parts represented by a rotation and length scaling.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 8, 2019
From: BLACK, MICHAEL J.; FREIFELD, OREN; WEISS, ALEXANDER W.; LOPER, MATTHEW M.; GUAN, PENG
To: BROWN UNIVERSITY
Reel/Frame 048274/0018 →
CONFIRMATORY LICENSE Recorded Sep 21, 2017
From: BROWN UNIVERSITY
To: NATIONAL INSTITUTES OF HEALTH (NIH), U.S. DEPT. OF HEALTH AND HUMAN SERVICES (DHHS), U.S. GOVERNMENT
Reel/Frame 043933/0661 →
Continuity (5)
Continuation 15342225 · Nov 3, 2016
Continuation 15042353 · Feb 12, 2016
Continuation 13696676
Provisional Application 61353407 · Jun 10, 2010
Related Publication 20180122146A1 · May 3, 2018
Cited By (1)
US 12,586,239