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In the realm of 3D digital human applications, music-to-dance presents achallenging task. Given the one-to-many relationship between music and dance,previous methods have been limited in their approach, relying solely onmatching and generating corresponding dance movements based on music rhythm. Inthe professional field of choreography, a dance phrase consists of severaldance poses and dance movements. Dance poses composed of a series of basicmeaningful body postures, while dance movements can reflect dynamic changessuch as the rhythm, melody, and style of dance. Taking inspiration from theseconcepts, we introduce an innovative dance generation pipeline calledDanceMeld, which comprising two stages, i.e., the dance decouple stage and thedance generation stage. In the decouple stage, a hierarchical VQ-VAE is used todisentangle dance poses and dance movements in different feature space levels,where the bottom code represents dance poses, and the top code represents dancemovements. In the generation stage, we utilize a diffusion model as a prior tomodel the distribution and generate latent codes conditioned on music features.We have experimentally demonstrated the representational capabilities of topcode and bottom code, enabling the explicit decoupling expression of danceposes and dance movements. This disentanglement not only provides control overmotion details, styles, and rhythm but also facilitates applications such asdance style transfer and dance unit editing. Our approach has undergonequalitative and quantitative experiments on the AIST++ dataset, demonstratingits superiority over other methods.

DanceMeld: Unraveling Dance Phrases with Hierarchical Latent Codes for Music-to-Dance Synthesis