Learn2Dance: Learning statistical music-to-dance mappings for choreography synthesis

Ferda Ofli, Engin Erzin, Yücel Yemez, A. Murat Tekalp

Research output: Contribution to journalArticle

13 Citations (Scopus)


We propose a novel framework for learning many-to-many statistical mappings from musical measures to dance figures towards generating plausible music-driven dance choreographies. We obtain music-to-dance mappings through use of four statistical models: 1) musical measure models, representing a many-to-one relation, each of which associates different melody patterns to a given dance figure via a hidden Markov model (HMM); 2) exchangeable figures model, which captures the diversity in a dance performance through a one-to-many relation, extracted by unsupervised clustering of musical measure segments based on melodic similarity; 3) figure transition model, which captures the intrinsic dependencies of dance figure sequences via an $n$-gram model; 4) dance figure models, capturing the variations in the way particular dance figures are performed, by modeling the motion trajectory of each dance figure via an HMM. Based on the first three of these statistical mappings, we define a discrete HMM and synthesize alternative dance figure sequences by employing a modified Viterbi algorithm. The motion parameters of the dance figures in the synthesized choreography are then computed using the dance figure models. Finally, the generated motion parameters are animated synchronously with the musical audio using a 3-D character model. Objective and subjective evaluation results demonstrate that the proposed framework is able to produce compelling music-driven choreographies.

Original languageEnglish
Article number6112231
Pages (from-to)747-759
Number of pages13
JournalIEEE Transactions on Multimedia
Issue number3 PART 2
Publication statusPublished - 22 May 2012


  • Automatic dance choreography creation
  • multimodal dance modeling
  • music-driven dance performance synthesis and animation
  • music-to-dance mapping
  • musical measure clustering

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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