RAMP: Rhythm-Adaptive Modeling with Progressive Teacher Regularization for Dance-to-Music Generation

Dance-to-music (D2M) generation requires fine-grained motion-rhythm control while maintaining coherent and semantically controllable music. We introduce RAMP, a framework for incorporating structured dance rhythm into a pretrained text-to-music (T2M) model. Its core component, the Style-Adaptive Rhythm Tokenizer (SART), performs global-to-local routing over shared rhythm-biased experts: a style-conditioned routing prototype captures clip-level rhythmic preferences, while motion-conditioned residual routing follows frame-level variation. To learn from these new conditions, RAMP uses teacher-regularized multimodal adaptation: it first adapts to the target text and lyric distribution, then introduces dance-aware conditioning while regularizing text-only velocity predictions toward a frozen Stage-1 teacher. The backbone's native text and lyric pathways remain explicit controls for high-level musical intent. We further construct Dance100K and a fixed 3K evaluation benchmark. Experiments show strong rhythmic alignment together with competitive semantic consistency and perceptual quality, supporting structured rhythm routing and teacher-regularized adaptation as complementary designs for controllable D2M generation. Anonymous project page：https://anonymous.4open.science/w/RAMP-Project.