Publication: Unsupervised dance figure analysis from video for dancing avatar animation
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Erdem, C. E.
Erdem, A. T.
Abaci, T.
Ozkan, M. K.
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Abstract
This paper presents a framework for unsupervised video analysis in the context of dance performances, where gestures and 3D movements of a dancer are characterized by repetition of a set of unknown dance figures. The system is trained in an unsupervised manner using Hidden Markov Models (HMMs) to automatically segment multi-view video recordings of a dancer into recurring elementary temporal body motion patterns to identify the dance figures. That is, a parallel HMM structure is employed to automatically determine the number and the temporal boundaries of different dance figures in a given dance video. The success of the analysis framework has been evaluated by visualizing these dance figures on a dancing avatar animated by the computed 3D analysis parameters. Experimental results demonstrate that the proposed framework enables synthetic agents and/or robots to learn dance figures from video automatically.
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Publisher
IEEE
Subject
Computer science, Artificial intelligence, Engineering, Electrical and electronic engineering, Imaging science, Photographic technology
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2008 15th IEEE International Conference on Image Processing, Vols 1-5
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DOI
10.1109/ICIP.2008.4712047
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