Human walking motion synthesis based on multiple regression hidden semi-Markov model

Abstract

This paper describes a statistical approach for modeling and synthesizing human walking motion. In the approach, each motion primitive is modeled statistically from motion capture data using multiple regression hidden semi-Markov model (HSMM). HSMM is an extension of hidden Markov model (HMM), in which each state has an explicit state duration probability distribution, and multiple regression HSMM is the one whose mean parameter of probability distribution function is assumed to be given by a function of factors which affects human motion. In this paper, we introduce a training algorithm for the multiple regression HSMM, called factor adaptive training based on the EM algorithm and also describe a parameter generation algorithm from motion primitive HSMMs with prescribed values of factors. From experimental results, we show that the proposed technique can control walking movements in accordance with a change of the factors such as walking pace and stride length and can provide realistic human motion.