@inproceedings{f2ebd12765954cc5932bf0f7a18873f8,
title = "Unsupervised dance figure analysis from video for dancing avatar animation",
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.",
keywords = "Dance figure identification, Dancing avatar animation, Unsupervised human body motion analysis",
author = "F. Ofti and E. Erzin and Y. Yemez and Tekalp, \{A. M.\} and Erdem, \{{\c C} E.\} and Erdem, \{A. T.\} and T. Abaci and {\"O}zkan, \{M. K.\}",
year = "2008",
doi = "10.1109/ICIP.2008.4712047",
language = "English",
isbn = "1424417643",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "1484--1487",
booktitle = "2008 IEEE International Conference on Image Processing, ICIP 2008 Proceedings",
note = "2008 IEEE International Conference on Image Processing, ICIP 2008 ; Conference date: 12-10-2008 Through 15-10-2008",
}