# Generative Autoregressive Networks for 3D Dancing Move Synthesis from Music

arXiv: https://arxiv.org/abs/1911.04069 · HTML: https://mlsanity.com/paper/1911.04069/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [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)
- [TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration](https://mlsanity.com/paper/2304.02419/) (2023)
- [Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure](https://mlsanity.com/paper/2111.12159/) (2021)
- [Music-Driven Group Choreography](https://mlsanity.com/paper/2303.12337/) (2023)
- [Controllable Group Choreography using Contrastive Diffusion](https://mlsanity.com/paper/2310.18986/) (2023)
- [Graph-based Normalizing Flow for Human Motion Generation and Reconstruction](https://mlsanity.com/paper/2104.03020/) (2021)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations](https://mlsanity.com/paper/2312.11442/) (2023)
- [Scalable Group Choreography via Variational Phase Manifold Learning](https://mlsanity.com/paper/2407.18839/) (2024)
- [TCDiff++: An End-to-end Trajectory-Controllable Diffusion Model for Harmonious Music-Driven Group Choreography](https://mlsanity.com/paper/2506.18671/) (2025)
- [Spatial-Temporal Graph Mamba for Music-Guided Dance Video Synthesis](https://mlsanity.com/paper/2507.06689/) (2025)
- [Dance Generation by Sound Symbolic Words](https://mlsanity.com/paper/2306.03646/) (2023)
- [May the Dance be with You: Dance Generation Framework for Non-Humanoids](https://mlsanity.com/paper/2405.19743/) (2024)

## Abstract (arXiv)

This paper proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components: a music feature encoder, a pose generator, and a music genre classifier. We focus on integrating these components for generating a realistic 3D human dancing move from music, which can be applied to artificial agents and humanoid robots. The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 5,000 pose frames. Experimental results of generated dance sequences from various songs show how the proposed method generates human-like dancing move to a given music. In addition, a generated 3D dance sequence is applied to a humanoid robot, showing that the proposed framework can make a robot to dance just by listening to music.