DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation

Understand

When hearing music, it is natural for people to dance to its rhythm.

• Automatic dance generation, however, is a challenging task due to the physical constraints of human motion and rhythmic alignment with target music.
• Conventional autoregressive methods introduce compounding errors during sampling and struggle to capture the long-term structure of dance sequences.
• To address these limitations, we present a novel cascaded motion diffusion model, DiffDance, designed for high-resolution, long-form dance generation.