Datasets:
license: cc-by-nc-4.0
task_categories:
- video-generation
- computer-vision
language:
- en
pretty_name: MACE-Dance
size_categories:
- 10K<n<100K
🎵 MACE-Dance Dataset
MACE-Dance is a large-scale dataset for music-driven dance video generation, released with our SIGGRAPH 2026 paper:
MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation
It is designed to support research on generating dance videos that are both:
- 🕺 kinematically plausible
- 🎨 visually coherent
- 🎼 well aligned with music
✨ Overview
The dataset contains approximately:
- 70K dance video clips
- 5–10 seconds per clip
- 116 hours in total
- 20+ dance genres
The data is curated from two complementary sources:
1. Motion-centric subset
- Derived from FineDance
- Front-view rendered dance videos from 3D motion sequences
- Focused on professional dance motion quality
2. Appearance-centric subset
- Collected from high-engagement internet dance videos
- Focused on visual appearance diversity and realism
This design helps benchmark both motion quality and appearance quality in music-driven dance video generation.
📂 Folder Structure
MACE-Dance/
├── Appearance/
└── Kinematic/
The exact file organization may vary depending on the released version.
🧹 Data Curation
For the in-the-wild subset, we apply a multi-stage cleaning pipeline:
- ✂️ shot boundary detection
- 🚶 motion filtering
- 🧍 single-person filtering
- ⏱️ clip segmentation into 5–10 second windows
This improves data quality for the music-driven dance generation task.
🎯 Intended Usage
This dataset is intended for research on:
- music-driven dance video generation
- music-driven 3D dance generation
- pose-driven / motion-driven human animation
- motion and appearance evaluation
⚠️ Notes
- This dataset is released for research purposes only.
- Please ensure your use complies with the corresponding license and platform policies.
- Some samples may originate from internet videos and are curated for academic research.
📚 Citation
If you find this dataset useful, please cite:
@article{yang2026macedance,
title={MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation},
author={Yang, Kaixing and Zhu, Jiashu and Tang, Xulong and Peng, Ziqiao and Zhang, Xiangyue and Wang, Puwei and Wu, Jiahong and Chu, Xiangxiang and Liu, Hongyan and He, Jun},
journal={ACM Transactions on Graphics (SIGGRAPH 2026)},
year={2026}
}