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Automatic 3D Motion Synthesis with Time-Striding Hidden Markov Model

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Book cover Advances in Machine Learning and Cybernetics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3930))

Abstract

In this paper we present a new method, time-striding hidden Markov model (TSHMM), to learn from long-term motion for atomic behaviors and the statistical dependencies among them. TSHMM is a 2-layer hidden Markov model, which approximates a variable-length hidden Markov model by first-order statistical dependencies. An EM algorithm is proposed to learn the TSHMM.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Graduate School at Shenzhen, China

    Yi Wang & Li-zhu Zhou

  2. School of Creative Media, City University of Hong Kong, Hong Kong, China

    Zhi-qiang Liu

Authors
  1. Yi Wang
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  2. Zhi-qiang Liu
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  3. Li-zhu Zhou
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Editor information

Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, P.O. Box, Hong Kong, China

    Daniel S. Yeung

  2. School of Creative Media, City University of Hong Kong,, China

    Zhi-Qiang Liu

  3. Department of Mathematics and Computer Science, Hebei University, 071002, Baoding, Hebei, P.R. China

    Xi-Zhao Wang

  4. School of Electrical and Information Engineering, University of Sydney, 2006, NSW, Australia

    Hong Yan

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, Y., Liu, Zq., Zhou, Lz. (2006). Automatic 3D Motion Synthesis with Time-Striding Hidden Markov Model. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_58

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  • DOI: https://doi.org/10.1007/11739685_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33584-9

  • Online ISBN: 978-3-540-33585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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