Human Walking Motion Synthesis with Desired Pace and Stride Length Based on HSMM


IEICE TRANSACTIONS on Information and Systems   Vol.E88-D    No.11    pp.2492-2499
Publication Date: 2005/11/01
Online ISSN: 
DOI: 10.1093/ietisy/e88-d.11.2492
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Life-like Agent and its Communication)
motion synthesis,  walking motion,  hidden semi-Markov Model (HSMM),  HMM,  factor adaptive training,  motion primitives,  motion capture,  

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This paper presents a new technique for automatically synthesizing human walking motion. In the technique, a set of fundamental motion units called motion primitives is defined and each primitive is modeled statistically from motion capture data using a hidden semi-Markov model (HSMM), which is a hidden Markov model (HMM) with explicit state duration probability distributions. The mean parameter for the probability distribution function of HSMM is assumed to be given by a function of factors that control the walking pace and stride length, and a training algorithm, called factor adaptive training, is derived based on the EM algorithm. A parameter generation algorithm from motion primitive HSMMs with given control factors is also described. Experimental results for generating walking motion are presented when the walking pace and stride length are changed. The results show that the proposing technique can generate smooth and realistic motion, which are not included in the motion capture data, without the need for smoothing or interpolation.

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