CDCGM: Composition-specified Dance Choreography Generation from Music

Significant research attention has recently been focused on the automatic generation of human dance choreography from music. While several generation models have been proposed, they cannot specify what kind of movements to generate, and as a result, random movements are generated. We therefore propose a generation model called Composition-specified Dance Choreography Generation from Music (CDCGM) that enables creators to specify which dance composition (i.e., type of movement) to take when generating a dance at each time step. We implemented CDCGM by first constructing a new dataset that includes motion captures of breakdancing and time-series annotation data of representative movement types. Evaluation experiments using our corpus showed that CDCGM can generate dances that faithfully reflect the specified dance composition with high quality. Compared to conventional state-of-the-art models, CDCGM is capable of generating quality dances that improve the expressiveness and the degree to which the dance matches the content and timing of the music. We also propose a new application for CDCGM in which users watch newly generated dance choreography simply by entering music and dance composition. The results of a user study evaluation of the application demonstrated that users found the experience of generating dance by specifying any dance composition for any music extremely fun, that it has the potential to greatly contribute to dance choreography and learning, and that there is a strong desire to use this application on a daily basis.