# Dance2Music: Automatic Dance-driven Music Generation

arXiv: https://arxiv.org/abs/2107.06252 · HTML: https://mlsanity.com/paper/2107.06252/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2021.

## Built on by (most cited first, 16 of 16)

- [Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression](https://mlsanity.com/paper/2411.16575/) (2024)
- [AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars](https://mlsanity.com/paper/2205.08535/) (2022)
- [OMG: Towards Open-vocabulary Motion Generation via Mixture of Controllers](https://mlsanity.com/paper/2312.08985/) (2023)
- [Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training](https://mlsanity.com/paper/2210.15929/) (2022)
- [Discrete Contrastive Diffusion for Cross-Modal Music and Image Generation](https://mlsanity.com/paper/2206.07771/) (2022)
- [Quantized GAN for Complex Music Generation from Dance Videos](https://mlsanity.com/paper/2204.00604/) (2022)
- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [Vision+X: A Survey on Multimodal Learning in the Light of Data](https://mlsanity.com/paper/2210.02884/) (2022)
- [UniMuMo: Unified Text, Music and Motion Generation](https://mlsanity.com/paper/2410.04534/) (2024)
- [DiffusionPhase: Motion Diffusion in Frequency Domain](https://mlsanity.com/paper/2312.04036/) (2023)
- [MuVi: Video-to-Music Generation with Semantic Alignment and Rhythmic Synchronization](https://mlsanity.com/paper/2410.12957/) (2024)
- [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024)
- [Soundify: Matching Sound Effects to Video](https://mlsanity.com/paper/2112.09726/) (2021)
- [A Comprehensive Survey on Generative AI for Video-to-Music Generation](https://mlsanity.com/paper/2502.12489/) (2025)
- [Vision-to-Music Generation: A Survey](https://mlsanity.com/paper/2503.21254/) (2025)
- [PF-D2M: A Pose-free Diffusion Model for Universal Dance-to-Music Generation](https://mlsanity.com/paper/2601.15872/) (2026)

## Abstract (arXiv)

Dance and music typically go hand in hand. The complexities in dance, music, and their synchronisation make them fascinating to study from a computational creativity perspective. While several works have looked at generating dance for a given music, automatically generating music for a given dance remains under-explored. This capability could have several creative expression and entertainment applications. We present some early explorations in this direction. We present a search-based offline approach that generates music after processing the entire dance video and an online approach that uses a deep neural network to generate music on-the-fly as the video proceeds. We compare these approaches to a strong heuristic baseline via human studies and present our findings. We have integrated our online approach in a live demo! A video of the demo can be found here: https://sites.google.com/view/dance2music/live-demo.