# Music-Driven Group Choreography

arXiv: https://arxiv.org/abs/2303.12337 · HTML: https://mlsanity.com/paper/2303.12337/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 69 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://mlsanity.com/paper/1506.03099/) (2015)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Learning Trajectory Dependencies for Human Motion Prediction](https://mlsanity.com/paper/1908.05436/) (2019)
- [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021)
- [Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory](https://mlsanity.com/paper/2203.13055/) (2022)
- [Single-Image Depth Perception in the Wild](https://mlsanity.com/paper/1604.03901/) (2016)
- [Coherent Reconstruction of Multiple Humans from a Single Image](https://mlsanity.com/paper/2006.08586/) (2020)
- [Convolutional Sequence to Sequence Model for Human Dynamics](https://mlsanity.com/paper/1805.00655/) (2018)
- [Multi-Person 3D Motion Prediction with Multi-Range Transformers](https://mlsanity.com/paper/2111.12073/) (2021)
- [Socially and Contextually Aware Human Motion and Pose Forecasting](https://mlsanity.com/paper/2007.06843/) (2020)
- [Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking](https://mlsanity.com/paper/2106.03772/) (2021)
- [Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio](https://mlsanity.com/paper/2011.12999/) (2020)
- [ChoreoNet: Towards Music to Dance Synthesis with Choreographic Action Unit](https://mlsanity.com/paper/2009.07637/) (2020)
- [Uncertainty-aware Label Distribution Learning for Facial Expression Recognition](https://mlsanity.com/paper/2209.10448/) (2022)
- [Weakly Supervised Deep Recurrent Neural Networks for Basic Dance Step Generation](https://mlsanity.com/paper/1807.01126/) (2018)
- [Global-Local Attention for Emotion Recognition](https://mlsanity.com/paper/2111.04129/) (2021)
- [Dance Style Transfer with Cross-modal Transformer](https://mlsanity.com/paper/2208.09406/) (2022)
- [GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction](https://mlsanity.com/paper/2110.00380/) (2021)
- [Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music](https://mlsanity.com/paper/1911.04069/) (2019)
- [Graph-based Person Signature for Person Re-Identifications](https://mlsanity.com/paper/2104.06770/) (2021)
- [When Creators Meet the Metaverse: A Survey on Computational Arts](https://mlsanity.com/paper/2111.13486/) (2021)
- [Human Interaction Recognition Framework based on Interacting Body Part Attention](https://mlsanity.com/paper/2101.08967/) (2021)
- [An Extensive Review of Computational Dance Automation Techniques and Applications](https://mlsanity.com/paper/1906.00606/) (2019)

## Built on by (most cited first, 30 of 37)

- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data](https://mlsanity.com/paper/2507.07095/) (2025)
- [FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis](https://mlsanity.com/paper/2405.15763/) (2024)
- [in2IN: Leveraging individual Information to Generate Human INteractions](https://mlsanity.com/paper/2404.09988/) (2024)
- [Towards Open Domain Text-Driven Synthesis of Multi-Person Motions](https://mlsanity.com/paper/2405.18483/) (2024)
- [Ready-to-React: Online Reaction Policy for Two-Character Interaction Generation](https://mlsanity.com/paper/2502.20370/) (2025)
- [ChainHOI: Joint-based Kinematic Chain Modeling for Human-Object Interaction Generation](https://mlsanity.com/paper/2503.13130/) (2025)
- [Dance with You: The Diversity Controllable Dancer Generation via Diffusion Models](https://mlsanity.com/paper/2308.13551/) (2023)
- [X-Dancer: Expressive Music to Human Dance Video Generation](https://mlsanity.com/paper/2502.17414/) (2025)
- [SocialGen: Modeling Multi-Human Social Interaction with Language Models](https://mlsanity.com/paper/2503.22906/) (2025)
- [Language-driven Scene Synthesis using Multi-conditional Diffusion Model](https://mlsanity.com/paper/2310.15948/) (2023)
- [Synergy and Synchrony in Couple Dances](https://mlsanity.com/paper/2409.04440/) (2024)
- [DanceCamera3D: 3D Camera Movement Synthesis with Music and Dance](https://mlsanity.com/paper/2403.13667/) (2024)
- [MoSa: Motion Generation with Scalable Autoregressive Modeling](https://mlsanity.com/paper/2511.01200/) (2025)
- [UniAct: Unified Motion Generation and Action Streaming for Humanoid Robots](https://mlsanity.com/paper/2512.24321/) (2025)
- [TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size](https://mlsanity.com/paper/2603.07988/) (2026)
- [LM2D: Lyrics- and Music-Driven Dance Synthesis](https://mlsanity.com/paper/2403.09407/) (2024)
- [Scalable Group Choreography via Variational Phase Manifold Learning](https://mlsanity.com/paper/2407.18839/) (2024)
- [FG-CXR: A Radiologist-Aligned Gaze Dataset for Enhancing Interpretability in Chest X-Ray Report Generation](https://mlsanity.com/paper/2411.15413/) (2024)
- [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)
- [OmniMotion-X: Versatile Multimodal Whole-Body Motion Generation](https://mlsanity.com/paper/2510.19789/) (2025)
- [The MI-Motion Dataset and Benchmark for 3D Multi-Person Motion Prediction](https://mlsanity.com/paper/2306.13566/) (2023)
- [TokenDance: Token-to-Token Music-to-Dance Generation with Bidirectional Mamba](https://mlsanity.com/paper/2603.27314/) (2026)
- [Multi-Modal Motion Retrieval by Learning a Fine-Grained Joint Embedding Space](https://mlsanity.com/paper/2507.23188/) (2025)
- [EgoMusic-driven Human Dance Motion Estimation with Skeleton Mamba](https://mlsanity.com/paper/2508.10522/) (2025)
- [Language-Guided Transformer Tokenizer for Human Motion Generation](https://mlsanity.com/paper/2602.08337/) (2026)
- [MDSC: Towards Evaluating the Style Consistency Between Music and Dance](https://mlsanity.com/paper/2309.01340/) (2023)
- [Motion Generation Review: Exploring Deep Learning for Lifelike Animation with Manifold](https://mlsanity.com/paper/2412.10458/) (2024)
- [ST-GDance: Long-Term and Collision-Free Group Choreography from Music](https://mlsanity.com/paper/2507.21518/) (2025)

## Abstract (arXiv)

Music-driven choreography is a challenging problem with a wide variety of industrial applications. Recently, many methods have been proposed to synthesize dance motions from music for a single dancer. However, generating dance motion for a group remains an open problem. In this paper, we present rm AIOZ-GDANCE, a new large-scale dataset for music-driven group dance generation. Unlike existing datasets that only support single dance, our new dataset contains group dance videos, hence supporting the study of group choreography. We propose a semi-autonomous labeling method with humans in the loop to obtain the 3D ground truth for our dataset. The proposed dataset consists of 16.7 hours of paired music and 3D motion from in-the-wild videos, covering 7 dance styles and 16 music genres. We show that naively applying single dance generation technique to creating group dance motion may lead to unsatisfactory results, such as inconsistent movements and collisions between dancers. Based on our new dataset, we propose a new method that takes an input music sequence and a set of 3D positions of dancers to efficiently produce multiple group-coherent choreographies. We propose new evaluation metrics for measuring group dance quality and perform intensive experiments to demonstrate the effectiveness of our method. Our project facilitates future research on group dance generation and is available at: https://aioz-ai.github.io/AIOZ-GDANCE/