# Exploring Multi-Modal Control in Music-Driven Dance Generation

arXiv: https://arxiv.org/abs/2401.01382 · HTML: https://mlsanity.com/paper/2401.01382/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 1 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos](https://mlsanity.com/paper/2304.01186/) (2023)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory](https://mlsanity.com/paper/2203.13055/) (2022)
- [FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation](https://mlsanity.com/paper/2212.03741/) (2022)
- [Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning](https://mlsanity.com/paper/2006.06119/) (2020)
- [TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration](https://mlsanity.com/paper/2304.02419/) (2023)
- [Consistent123: One Image to Highly Consistent 3D Asset Using Case-Aware Diffusion Priors](https://mlsanity.com/paper/2309.17261/) (2023)

## Built on by (most cited first, 10 of 10)

- [Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives](https://mlsanity.com/paper/2403.10518/) (2024)
- [X-Dancer: Expressive Music to Human Dance Video Generation](https://mlsanity.com/paper/2502.17414/) (2025)
- [Novel Object Synthesis via Adaptive Text-Image Harmony](https://mlsanity.com/paper/2410.20823/) (2024)
- [FineMotion: A Dataset and Benchmark with both Spatial and Temporal Annotation for Fine-grained Motion Generation and Editing](https://mlsanity.com/paper/2507.19850/) (2025)
- [MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation](https://mlsanity.com/paper/2512.18181/) (2025)
- [Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis](https://mlsanity.com/paper/2504.09885/) (2025)
- [TCDiff++: An End-to-end Trajectory-Controllable Diffusion Model for Harmonious Music-Driven Group Choreography](https://mlsanity.com/paper/2506.18671/) (2025)
- [MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation](https://mlsanity.com/paper/2508.16911/) (2025)
- [Harmonious Group Choreography with Trajectory-Controllable Diffusion](https://mlsanity.com/paper/2403.06189/) (2024)
- [ReactMotion: Generating Reactive Listener Motions from Speaker Utterance](https://mlsanity.com/paper/2603.15083/) (2026)

## Abstract (arXiv)

Existing music-driven 3D dance generation methods mainly concentrate on high-quality dance generation, but lack sufficient control during the generation process. To address these issues, we propose a unified framework capable of generating high-quality dance movements and supporting multi-modal control, including genre control, semantic control, and spatial control. First, we decouple the dance generation network from the dance control network, thereby avoiding the degradation in dance quality when adding additional control information. Second, we design specific control strategies for different control information and integrate them into a unified framework. Experimental results show that the proposed dance generation framework outperforms state-of-the-art methods in terms of motion quality and controllability.