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Learn2Dance: Learning Statistical Music-to-Dance Mappings for Choreography Synthesis
2012
IEEE transactions on multimedia
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
doi:10.1109/tmm.2011.2181492
fatcat:uavnh4bvq5bnxmvzcxvzgb3epm