UNSUPERVISED DANCE FIGURE ANALYSIS FROM VIDEO FOR DANCING AVATAR ANIMATION


Ofli F., Erzin E., Yemez Y., Tekalp A. M. , Erdem Ç. , Erdem A. T. , ...Daha Fazla

15th IEEE International Conference on Image Processing (ICIP 2008), California, Amerika Birleşik Devletleri, 12 - 15 Ekim 2008, ss.1484-1487 identifier identifier

  • Doi Numarası: 10.1109/icip.2008.4712047
  • Basıldığı Şehir: California
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.1484-1487

Özet

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.