Dancing with turks

I. Kao Chiang, Ian Spiro, Seungkyu Lee, Alyssa Lees, Jingchen Liu, Chris Bregler, Yanxi Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Dance is a dynamic art form that reects a wide range of cultural diversity and individuality. With the advancement of motion-capture technology combined with crowd-sourcing and machine learning algorithms, we explore the complex relationship between perceived dance quality/dancer's gender and dance movements/music respectively. As a feasibility study, we construct a computational framework for an analysis-synthesis-feedback loop using a novel multimedia dance-music texture representation. Furthermore, we integrate crowd-sourcing, music and motion-capture data, and machine learning-based methods for dance segmentation, analysis and synthesis of new dancers. A quantitative validation of this framework on a motion-capture dataset of 172 dancers evaluated by more than 400 independent on-line raters demonstrates significant correlation between human perception and the algorithmically intended dance quality or gender of synthesized dancers. The technology illustrated in this work has a high potential to advance the multimedia entertainment industry via dancing with Turks.

Original languageEnglish (US)
Title of host publicationMM 2015 - Proceedings of the 2015 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages10
ISBN (Electronic)9781450334594
StatePublished - Oct 13 2015
Event23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
Duration: Oct 26 2015Oct 30 2015

Publication series

NameMM 2015 - Proceedings of the 2015 ACM Multimedia Conference


Other23rd ACM International Conference on Multimedia, MM 2015

All Science Journal Classification (ASJC) codes

  • Media Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software


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