# Music2Dance: DanceNet for Music-driven Dance Generation

arXiv: https://arxiv.org/abs/2002.03761 · HTML: https://mlsanity.com/paper/2002.03761/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2020.

## Built on by (most cited first, 6 of 6)

- [MotionGPT: Finetuned LLMs Are General-Purpose Motion Generators](https://mlsanity.com/paper/2306.10900/) (2023)
- [Dance with You: The Diversity Controllable Dancer Generation via Diffusion Models](https://mlsanity.com/paper/2308.13551/) (2023)
- [PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance](https://mlsanity.com/paper/2406.09326/) (2024)
- [ChoreoMuse: Robust Music-to-Dance Video Generation with Style Transfer and Beat-Adherent Motion](https://mlsanity.com/paper/2507.19836/) (2025)
- [Learning Human Motion with Temporally Conditional Mamba](https://mlsanity.com/paper/2510.12573/) (2025)
- [TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation](https://mlsanity.com/paper/2604.17005/) (2026)

## Abstract (arXiv)

Synthesize human motions from music, i.e., music to dance, is appealing and attracts lots of research interests in recent years. It is challenging due to not only the requirement of realistic and complex human motions for dance, but more importantly, the synthesized motions should be consistent with the style, rhythm and melody of the music. In this paper, we propose a novel autoregressive generative model, DanceNet, to take the style, rhythm and melody of music as the control signals to generate 3D dance motions with high realism and diversity. To boost the performance of our proposed model, we capture several synchronized music-dance pairs by professional dancers, and build a high-quality music-dance pair dataset. Experiments have demonstrated that the proposed method can achieve the state-of-the-art results.