2026 Volume 34 Pages 2-13
We propose a novel dance generation technology called Dance Choreography Generation from Music with Dancer-specific Style (SDCGM) that automatically generates new dance choreography to match a specified music track and dancer-specific style. This technology is achieved through a transformer-based conditional diffusion model framework, where both music and style information are utilized as input conditions. The results of evaluation experiments indicate that compared to existing state-of-the-art models, the dances generated by the SDCGM model exhibit higher quality in terms of naturalness and are more capable of reflecting the specified dancer-specific style appropriately. Furthermore, user evaluations of a prototype application suggest that the dance generation functionality provided by SDCGM could be beneficial for professional dancers in terms of mastering dances and supporting choreography creation. By utilizing SDCGM, anyone can specify their favorite music and dancer-specific style to generate a wide variety of attractive dances.
