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Physics-Based Human Motion Models for Animation and Tracking

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describe physics-based models of human motion, with applications to computer animation and 3D person tracking. I begin by surveying relevant principles of motion from the biomechanics literature. I then describe a representation of motion that incorporates passive dynamical elements, relative muscle preferences, and optimality principles. I then describe Nonlinear Inverse Optimization, a novel learning algorithm that can be used to learn these physical models of motion from data. Once learned, these models can then be synthesize new animation. I then describe a tracking algorithm for on-line estimation of 3D human motion from video sequences that makes use of a physics-based prior model of motion.

This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.

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