center for robotics and embedded systems University of Southern California Viterbi School of Engineering

In this paper we address the problem of automatically deriving vocabularies of motion modules from human motion data, taking advantage of the underlying structure in motion. We approach this problem with a data-driven methodology for modularizing a motion stream (or time-series of human motion) into a vocabulary of parameterized actions and a set of high-level behaviors for sequencing actions. Central to this methodology is the discovery of spatio-temporal structure in a motion stream. We estimate this structure by using a spatio-temporal dimension reduction method based on extended Isomap. The utility of the derived vocabularies is validated through their use in synthesizing new humanoid motion.


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