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17. Bodies In Motion: Dynamic Motion Capture

This video was recorded at AAAI 2009: AI Video Competition. We explore the use of full-body 3D physical simulation for human kinematic tracking from monocular and multi-view video sequences within the Bayesian filtering framework. Towards greater physical plausibility, we consider a human's motion to be generated by a "feedback control loop", where Newtonian physics approximates the rigid-body motion dynamics of the human and the environment through the application and integration of forces. The result is more faithful modeling of human-environment interactions, such as ground contacts, resulting from collisions and the human's motor control. For more information, please see: http://robotics.cs.brown.edu/projects/dynamical_tracking/.

Keywords:: videolectures, ocwc, oec

Disciplines:

Science and Technology / Computer Science / Intelligent Systems

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Material Type:: Presentation
Date Added to MERLOT:: February 10, 2015
Date Modified in MERLOT:: February 10, 2015
Authors:: Odest Chadwicke Jenkins, Department of Computer Science, Brown University
Leonid Sigal, Department of Computer Science, University of Toronto
Marek Vondrak, Department of Computer Science, Brown University
Daniel Byers, Department of Computer Science, Brown University
Submitter:: The Open Education Consortium
Primary Audience:: College General Ed, College Lower Division, College Upper Division
Technical Format:: Video

Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States