# EnchantDance: Unveiling the Potential of Music-Driven Dance Movement

arXiv: https://arxiv.org/abs/2312.15946 · HTML: https://mlsanity.com/paper/2312.15946/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2023.

## Built on by (most cited first, 7 of 7)

- [TCDiff++: An End-to-end Trajectory-Controllable Diffusion Model for Harmonious Music-Driven Group Choreography](https://mlsanity.com/paper/2506.18671/) (2025)
- [Harmonious Group Choreography with Trajectory-Controllable Diffusion](https://mlsanity.com/paper/2403.06189/) (2024)
- [MCM: Multi-condition Motion Synthesis Framework](https://mlsanity.com/paper/2404.12886/) (2024)
- [Global Position Aware Group Choreography using Large Language Model](https://mlsanity.com/paper/2503.09645/) (2025)
- [OpenDance: Multimodal Controllable 3D Dance Generation with Large-scale Internet Data](https://mlsanity.com/paper/2506.07565/) (2025)
- [Learning Quantised Structure-Preserving Motion Representations for Dance Fingerprinting](https://mlsanity.com/paper/2604.00927/) (2026)
- [Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation](https://mlsanity.com/paper/2606.22726/) (2026)

## Abstract (arXiv)

The task of music-driven dance generation involves creating coherent dance movements that correspond to the given music. While existing methods can produce physically plausible dances, they often struggle to generalize to out-of-set data. The challenge arises from three aspects: 1) the high diversity of dance movements and significant differences in the distribution of music modalities, which make it difficult to generate music-aligned dance movements. 2) the lack of a large-scale music-dance dataset, which hinders the generation of generalized dance movements from music. 3) The protracted nature of dance movements poses a challenge to the maintenance of a consistent dance style. In this work, we introduce the EnchantDance framework, a state-of-the-art method for dance generation. Due to the redundancy of the original dance sequence along the time axis, EnchantDance first constructs a strong dance latent space and then trains a dance diffusion model on the dance latent space. To address the data gap, we construct a large-scale music-dance dataset, ChoreoSpectrum3D Dataset, which includes four dance genres and has a total duration of 70.32 hours, making it the largest reported music-dance dataset to date. To enhance consistency between music genre and dance style, we pre-train a music genre prediction network using transfer learning and incorporate music genre as extra conditional information in the training of the dance diffusion model. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance on dance quality, diversity, and consistency.