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

  • Yi Wang
  • Zhi-qiang Liu
  • Li-zhu Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


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.


Hide Markov Model Training Sequence Atomic Behavior Context Tree Training Motion 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yi Wang
    • 1
  • Zhi-qiang Liu
    • 2
  • Li-zhu Zhou
    • 1
  1. 1.Department of Computer Science and TechnologyTsinghua University, Graduate School at ShenzhenChina
  2. 2.School of Creative MediaCity University of Hong KongHong KongChina

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