  Choreography refers to creation of dance steps and motions for dances
according to the latent knowledge in human mind, where the created dance
motions are in general style-specific and consistent. So far, such latent
style-specific knowledge about dance styles cannot be represented explicitly in
human language and has not yet been learned in previous works on music-to-dance
generation tasks. In this paper, we propose a novel music-to-dance synthesis
framework with controllable style embeddings. These embeddings are learned
representations of style-consistent kinematic abstraction of reference dance
clips, which act as controllable factors to impose style constraints on dance
generation in a latent manner. Thus, the dance styles can be transferred to
dance motions by merely modifying the style embeddings. To support this study,
we build a large music-to-dance dataset. The qualitative and quantitative
evaluations demonstrate the advantage of our proposed framework, as well as the
ability of synthesizing diverse styles of dances from identical music via style
embeddings.
