Learning Music-Dance Representations through Explicit-Implicit Rhythm Synchronization

Understand

Although audio-visual representation has been proved to be applicable in many downstream tasks, the representation of dancing videos, which is more specific and always accompanied by music with complex auditory contents, remains challenging and uninvestigated.

• Considering the intrinsic alignment between the cadent movement of dancer and music rhythm, we introduce MuDaR, a novel Music-Dance Representation learning framework to perform the synchronization of music and dance rhythms both in explicit and implicit ways.
• Specifically, we derive the dance rhythms based on visual appearance and motion cues inspired by the music rhythm analysis.
• Then the visual rhythms are temporally aligned with the music counterparts, which are extracted by the amplitude of sound intensity.