# Towards 3D Dance Motion Synthesis and Control

arXiv: https://arxiv.org/abs/2006.05743 · HTML: https://mlsanity.com/paper/2006.05743/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [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)
- [FixMyPose: Pose Correctional Captioning and Retrieval](https://mlsanity.com/paper/2104.01703/) (2021)
- [A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision](https://mlsanity.com/paper/2307.03353/) (2023)
- [MusicFace: Music-driven Expressive Singing Face Synthesis](https://mlsanity.com/paper/2303.14044/) (2023)
- [Dance Generation by Sound Symbolic Words](https://mlsanity.com/paper/2306.03646/) (2023)

## Abstract (arXiv)

3D human dance motion is a cooperative and elegant social movement. Unlike regular simple locomotion, it is challenging to synthesize artistic dance motions due to the irregularity, kinematic complexity and diversity. It requires the synthesized dance is realistic, diverse and controllable. In this paper, we propose a novel generative motion model based on temporal convolution and LSTM,TC-LSTM, to synthesize realistic and diverse dance motion. We introduce a unique control signal, dance melody line, to heighten controllability. Hence, our model, and its switch for control signals, promote a variety of applications: random dance synthesis, music-to-dance, user control, and more. Our experiments demonstrate that our model can synthesize artistic dance motion in various dance types. Compared with existing methods, our method achieved start-of-the-art results.