ChoreoMuse: Robust Music-to-Dance Video Generation with Style Transfer and Beat-Adherent Motion

TL;DR

ChoreoMuse is a diffusion-based framework that generates high-quality, style-controllable dance videos aligned with music beats and individual dancer characteristics, overcoming resolution constraints and supporting diverse musical genres.

Contribution

We introduce ChoreoMuse, a novel music-to-dance video generation method utilizing SMPL parameters and a new music encoder, achieving state-of-the-art results in style adherence and video quality.

Findings

Achieves high fidelity and beat alignment in generated dance videos.

Supports style control and diverse musical genres.

Outperforms existing methods in quality and stylistic accuracy.

Abstract

Modern artistic productions increasingly demand automated choreography generation that adapts to diverse musical styles and individual dancer characteristics. Existing approaches often fail to produce high-quality dance videos that harmonize with both musical rhythm and user-defined choreography styles, limiting their applicability in real-world creative contexts. To address this gap, we introduce ChoreoMuse, a diffusion-based framework that uses SMPL format parameters and their variation version as intermediaries between music and video generation, thereby overcoming the usual constraints imposed by video resolution. Critically, ChoreoMuse supports style-controllable, high-fidelity dance video generation across diverse musical genres and individual dancer characteristics, including the flexibility to handle any reference individual at any resolution. Our method employs a novel music encoder MotionTune to capture motion cues from audio, ensuring that the generated choreography closely follows the beat and expressive qualities of the input music. To quantitatively evaluate how well the generated dances match both musical and choreographic styles, we introduce two new metrics that measure alignment with the intended stylistic cues. Extensive experiments confirm that ChoreoMuse achieves state-of-the-art performance across multiple dimensions, including video quality, beat alignment, dance diversity, and style adherence, demonstrating its potential as a robust solution for a wide range of creative applications. Video results can be found on our project page: https://choreomuse.github.io.