Dynamical Models for Human Gait Synthesis
We present a novel approach to modeling human gaits such as walking and running. We represent the trajectories of a certain number of salient features on the human body as the output of a dynamical system that is made up of two subsystems, an autonomous subsystem and a subsystem driven by an unknown stochastic input. We present techniques for inferring model parameters and input signal distributions from data. In particular, we exploit the higher-order statistical information content in motion capture data to arrive at input signals with independent components. We show that we can synthesize arbitrarily long sequences of human gaits that capture the dynamic complexities and the distinct character of the original gait sequences.