🎵 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

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✨ 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.

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📂 Folder Structure

MACE-Dance/
├── Appearance/
└── Kinematic/

The exact file organization may vary depending on the released version.

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🧹 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.

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🎯 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

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⚠️ 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.

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📚 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}
}

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