Human Walking Motion Synthesis with Desired Pace and Stride Length Based on HSMM(<Special Section>Life-like Agent and its Communication)

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概要

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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.

• 社団法人電子情報通信学会の論文
• 2005-11-01

著者

• Yamagishi Junichi

  Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology

• Kobayashi Takao

  Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology

• NIWASE Naotake

  Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology

• Niwase Naotake

  Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology

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• FOREWORD
• A context clustering technique for improvement of tone intelligibility of average-voice-based Thai speech synthesis (Speech) -- (国際ワークショップ"Asian workshop on speech science and technology")

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