Abstract
We present a novel approach to modeling subtleties in human motion. We represent the trajectories of a certain number of salient features on the human body as the output of a dynamical system driven by an unknown stochastic input. We present several techniques for inferring model parameters and input signal distributions corresponding to different optimality criteria, and evaluate the corresponding models for accuracy and predictive power. In particular we exploit the higher order statistical information content in motion data to arrive at input signals with independent components and show that the human motion synthesized from non-Gaussian inputs capture best the subtle complexities of the motion data.
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