DanceMAR: Local Generation over Retrieved Motion Priors for Full-Body Dance Synthesis

Music-driven full-body dance synthesis requires realistic body and hand motion that follows the music and remains temporally coherent. Current methods primarily generate complete sequences or reuse retrieved examples, with less emphasis on local adaptation that preserves retrieved motion context. In this paper, we introduce DanceMAR, a framework that adapts retrieved motion priors through music-conditioned local residual generation. Specifically, we represent retrieved motion with a continuous hierarchy, retaining the lower-resolution context while restricting updates to local regions of the finest body-motion stream. To improve temporal coherence, we train a bidirectional masked model with local perturbations and motion-space supervision to learn residual corrections from the music and surrounding motion. In addition, we combine the adapted body with hand articulation from the same retrieved example to obtain full-body motion. Extensive experiments on FineDance demonstrate that DanceMAR achieves state-of-the-art body and hand motion fidelity while maintaining competitive beat alignment and foot stability. Further analyses show that motion changes remain localized, with smooth transitions between the adapted regions and the surrounding motion.