Forecasting Human Pose and Motion with Multibody Dynamic Model

Understanding human motion with dynamics is in its infancy, but it is a highly promising approach in computer vision, robotics and computer graphics. We propose a Multibody Dynamic Model (MDM) which estimates poses and motions through analyzing forces-the intrinsic motivation for motion. With the 23 degrees of freedom Multibody Dynamic Model, we analyze human motion dynamics in the whole body, and then forecast human motion or pose in occluded or non-captured circumstances. Our two main contributions are essential for understanding human motion with dynamics. The first one is to provide effective representations and computational models for dynamic analysis of human motion in the whole body, via the intrinsic connection between force and motion in the biomechanical system. The second contribution is to offer a more natural method to forecast pose and motion with the estimated forces. In our experiments, MDM has been successfully applied to running, jumping and other challenging sports activities.

[1]  Bernt Schiele,et al.  Pictorial structures revisited: People detection and articulated pose estimation , 2009, CVPR.

[2]  W. Blajer,et al.  Multibody modeling of human body for the inverse dynamics analysis of sagittal plane movements , 2007 .

[3]  E. Chao,et al.  Application of optimization principles in determining the applied moments in human leg joints during gait. , 1973, Journal of biomechanics.

[4]  Ben Taskar,et al.  Parsing human motion with stretchable models , 2011, CVPR 2011.

[5]  David A. Forsyth,et al.  Improved Human Parsing with a Full Relational Model , 2010, ECCV.

[6]  Yi Yang,et al.  Articulated pose estimation with flexible mixtures-of-parts , 2011, CVPR 2011.

[7]  Stan Sclaroff,et al.  Fast globally optimal 2D human detection with loopy graph models , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  David J. Fleet,et al.  The Kneed Walker for human pose tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[10]  C. E. Passerello,et al.  On human body dynamics , 1976, Annals of Biomedical Engineering.

[11]  Odest Chadwicke Jenkins,et al.  Physical simulation for probabilistic motion tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  W Blajer,et al.  Modeling and inverse simulation of somersaults on the trampoline. , 2001, Journal of biomechanics.

[13]  Reza N. Jazar,et al.  Theory of Applied Robotics: Kinematics, Dynamics, and Control , 2007 .

[14]  A. E. Chapman,et al.  Biomechanical Analysis of Fundamental Human Movements , 2008 .

[15]  David J. Fleet,et al.  Physics-Based Person Tracking Using Simplified Lower-Body Dynamics , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Jinxiang Chai,et al.  VideoMocap: modeling physically realistic human motion from monocular video sequences , 2010, ACM Trans. Graph..

[17]  Raquel Urtasun,et al.  Physically-based motion models for 3D tracking: A convex formulation , 2011, 2011 International Conference on Computer Vision.

[18]  Yang Wang,et al.  Multiple Tree Models for Occlusion and Spatial Constraints in Human Pose Estimation , 2008, ECCV.

[19]  Reza N. Jazar,et al.  Theory of Applied Robotics , 2007 .

[20]  Ramakant Nevatia,et al.  Efficient Inference with Multiple Heterogeneous Part Detectors for Human Pose Estimation , 2010, ECCV.

[21]  David J. Fleet,et al.  Estimating contact dynamics , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Ben Taskar,et al.  Cascaded Models for Articulated Pose Estimation , 2010, ECCV.