IP Library Granted Patent US 8,180,714
Granted Patent B2
US 8,180,714 · App. 12/156,132 · Granted May 15, 2012

Automatic generation of human models for motion capture, biomechanics and animation

Assignees: The Board of Trustees of the Leland Stanford Junior University; Politecnico di Milano
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Quick Facts
Patent No.
US 8,180,714
App. No.
12/156,132
Granted
May 15, 2012
Kind
B2
Abstract

An automated method for the generation of (i) human models comprehensive of shape and joint centers information and/or (ii) subject specific models from multiple video streams is provided. To achieve these objectives, a kinematic model is learnt space from a training data set. The training data set includes kinematic models associated with corresponding morphological models. A shape model is identified as well as one or more poses of the subject. The learnt kinematic model space and the identified shape model are combined to generate a full body model of the subject starting from as few as one-static pose. Further, to generate a full body model of an arbitrary human subject, the learnt kinematic model space and the identified shape model are combined using a parameter set. The invention is applicable for fully automatic markerless motion capture and generation of complete human models.

Claims (10)

1. A method for generating subject-specific human motion capture models having morphological and kinematic model information, comprising:

(a) learning a kinematic model space from a training data set, said training data set comprises kinematic models associated with corresponding morphological models;

(b) using a series of meshes describing human body shape to perform a shape registration algorithm for identifying and registering from a database a shape model of a human subject in one or more poses;

(c) combining said learnt kinematic model space and said identified shape model to generate a full body model of said human subject, said generating starts from as few as one of said poses identified in step (b); and

(d) combining said learnt kinematic model space and said identified shape model to generate a full body model of an arbitrary human subject, said generating using a parameter set.

2. The method as set forth in claim 1 , wherein said step of combining in 1(c) and/or 1(d) are based on matching to a three-dimensional representation of the subject.

3. The method as set forth in claim 1 , wherein said kinematic models define an articulated multi-segmented rigid body model.

4. The method as set forth in claim 1 , wherein said steps 1(a) through 1(d) are performed in a fully automated fashion.

5. The method as set forth in claim 1 , wherein said step 1(d) is performed in an automated fashion.

6. The method as set forth in claim 1 , wherein said step of combining in 1(c) and/or 1(d) further comprises imposing joint constraints.

Assignments (3)
CONFIRMATORY LICENSE Recorded Dec 30, 2009
From: STANFORD UNIVERSITY
To: NATIONAL SCIENCE FOUNDATION
Reel/Frame 023719/0512 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 20, 2008
From: GAMBARETTO, EMILIANO
To: POLITECNICO DI MILANO
Reel/Frame 021730/0535 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 20, 2008
From: CORAZZA, STEFANO; MUNDERMANN, LARS; ANDRIACCHI, THOMAS P.
To: BOARD OF TRUSTEES OF THE LELAND STANFORD JUNIOR UNIVERSITY, THE
Reel/Frame 021730/0619 →
Continuity (2)
Provisional Application 60932249 · May 29, 2007
Related Publication 20100020073A1 · Jan 28, 2010