# Learning to dance: A graph convolutional adversarial network to generate realistic dance motions from audio

arXiv: https://arxiv.org/abs/2011.12999 · HTML: https://mlsanity.com/paper/2011.12999/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 30 of 33)

- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory](https://mlsanity.com/paper/2203.13055/) (2022)
- [Synthesis of Compositional Animations from Textual Descriptions](https://mlsanity.com/paper/2103.14675/) (2021)
- [FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation](https://mlsanity.com/paper/2212.03741/) (2022)
- [TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration](https://mlsanity.com/paper/2304.02419/) (2023)
- [UDE: A Unified Driving Engine for Human Motion Generation](https://mlsanity.com/paper/2211.16016/) (2022)
- [Quantized GAN for Complex Music Generation from Dance Videos](https://mlsanity.com/paper/2204.00604/) (2022)
- [Music-Driven Group Choreography](https://mlsanity.com/paper/2303.12337/) (2023)
- [DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation](https://mlsanity.com/paper/2308.02915/) (2023)
- [It's Time for Artistic Correspondence in Music and Video](https://mlsanity.com/paper/2206.07148/) (2022)
- [Controllable Group Choreography using Contrastive Diffusion](https://mlsanity.com/paper/2310.18986/) (2023)
- [Dance Any Beat: Blending Beats with Visuals in Dance Video Generation](https://mlsanity.com/paper/2405.09266/) (2024)
- [LongDanceDiff: Long-term Dance Generation with Conditional Diffusion Model](https://mlsanity.com/paper/2308.11945/) (2023)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Hand-Centric Motion Refinement for 3D Hand-Object Interaction via Hierarchical Spatial-Temporal Modeling](https://mlsanity.com/paper/2401.15987/) (2024)
- [BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis](https://mlsanity.com/paper/2207.10120/) (2022)
- [PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance](https://mlsanity.com/paper/2406.09326/) (2024)
- [Scalable Group Choreography via Variational Phase Manifold Learning](https://mlsanity.com/paper/2407.18839/) (2024)
- [Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesis](https://mlsanity.com/paper/2507.06689/) (2025)
- [ChoreoMuse: Robust Music-to-Dance Video Generation with Style Transfer and Beat-Adherent Motion](https://mlsanity.com/paper/2507.19836/) (2025)
- [Creating and Reenacting Controllable 3D Humans with Differentiable Rendering](https://mlsanity.com/paper/2110.11746/) (2021)
- [Robust Dancer: Long-term 3D Dance Synthesis Using Unpaired Data](https://mlsanity.com/paper/2303.16856/) (2023)
- [Tempo as the Stable Cue: Hierarchical Mixture of Tempo and Beat Experts for Music to 3D Dance Generation](https://mlsanity.com/paper/2512.18804/) (2025)
- [TokenDance: Token-to-Token Music-to-Dance Generation with Bidirectional Mamba](https://mlsanity.com/paper/2603.27314/) (2026)
- [Improving Ultrasound Tongue Image Reconstruction from Lip Images Using Self-supervised Learning and Attention Mechanism](https://mlsanity.com/paper/2106.11769/) (2021)
- [Do as we do: Multiple Person Video-To-Video Transfer](https://mlsanity.com/paper/2104.04721/) (2021)
- [Diverse Dance Synthesis via Keyframes with Transformer Controllers](https://mlsanity.com/paper/2207.05906/) (2022)
- [Empowering Sign Language Communication: Integrating Sentiment and Semantics for Facial Expression Synthesis](https://mlsanity.com/paper/2408.15159/) (2024)
- [Dynamic Motion Synthesis: Masked Audio-Text Conditioned Spatio-Temporal Transformers](https://mlsanity.com/paper/2409.01591/) (2024)
- [ST-GDance: Long-Term and Collision-Free Group Choreography from Music](https://mlsanity.com/paper/2507.21518/) (2025)

## Abstract (arXiv)

Synthesizing human motion through learning techniques is becoming an increasingly popular approach to alleviating the requirement of new data capture to produce animations. Learning to move naturally from music, i.e., to dance, is one of the more complex motions humans often perform effortlessly. Each dance movement is unique, yet such movements maintain the core characteristics of the dance style. Most approaches addressing this problem with classical convolutional and recursive neural models undergo training and variability issues due to the non-Euclidean geometry of the motion manifold structure.In this paper, we design a novel method based on graph convolutional networks to tackle the problem of automatic dance generation from audio information. Our method uses an adversarial learning scheme conditioned on the input music audios to create natural motions preserving the key movements of different music styles. We evaluate our method with three quantitative metrics of generative methods and a user study. The results suggest that the proposed GCN model outperforms the state-of-the-art dance generation method conditioned on music in different experiments. Moreover, our graph-convolutional approach is simpler, easier to be trained, and capable of generating more realistic motion styles regarding qualitative and different quantitative metrics. It also presented a visual movement perceptual quality comparable to real motion data.