Title: Automatic 3D Motion Synthesis with Time-Striding Hidden Markov Model
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.
Publication Year: 2006
Publication Date: 2006-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 3
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