PhaseDance: Capturing Rhythm and Expressivity in Dance Modeling

Generating dance that reflects many aspects of music remains a fundamental challenge in dance motion synthesis. Existing music-conditioned approaches formulate the task as beat-level matching, which captures local synchrony but misses the compositional structure of rhythm, where movement is organized into global beat units. We argue that musical dance arises from the alignment of \emph{periodicity} between motion and music, not from instantaneous beat coincidence. To address this, we propose \textbf{PhaseDance}, a phase-conditioned framework that represents dance as a quasi-musical signal on periodic phase manifolds, enabling unified dance generation across multiple tasks. Alongside this, we ground text descriptions in fifteen music-correlated metrics derived from Laban Movement Analysis to capture the qualitative richness of dance, articulating structured dimensions of motion — effort, shape, and dynamics — that coarse genre labels cannot convey. Experiments show that PhaseDance produces choreography with stronger rhythmic coherence and richer expressive understanding than existing models.