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

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/icip.2008.4712047
  • Basıldığı Şehir: California
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.1484-1487
  • Anahtar Kelimeler: unsupervised human body motion analysis, dance figure identification, dancing avatar animation
  • Marmara Üniversitesi Adresli: Hayır

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.