Human Walking Motion Synthesis with Desired Pace and Stride Length Based on HSMM
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.11 pp.2492-2499
- Publication Date
- 2005/11/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.11.2492
- Type of Manuscript
- Special Section PAPER (Special Section on Life-like Agent and its Communication)
- Category
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Naotake NIWASE, Junichi YAMAGISHI, Takao KOBAYASHI, "Human Walking Motion Synthesis with Desired Pace and Stride Length Based on HSMM" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 11, pp. 2492-2499, November 2005, doi: 10.1093/ietisy/e88-d.11.2492.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.11.2492/_p
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@ARTICLE{e88-d_11_2492,
author={Naotake NIWASE, Junichi YAMAGISHI, Takao KOBAYASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Human Walking Motion Synthesis with Desired Pace and Stride Length Based on HSMM},
year={2005},
volume={E88-D},
number={11},
pages={2492-2499},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e88-d.11.2492},
ISSN={},
month={November},}
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TY - JOUR
TI - Human Walking Motion Synthesis with Desired Pace and Stride Length Based on HSMM
T2 - IEICE TRANSACTIONS on Information
SP - 2492
EP - 2499
AU - Naotake NIWASE
AU - Junichi YAMAGISHI
AU - Takao KOBAYASHI
PY - 2005
DO - 10.1093/ietisy/e88-d.11.2492
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2005
AB - 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.
ER -
English
