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

Dance Generation with Style Embedding: Learning and Transferring Latent Representations of Dance Styles