README.md

metadata

license: cc-by-4.0
pipeline_tag: image-to-video

MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation

Paper | Project Page | GitHub

MACE-Dance is a cascaded expert framework for music-driven dance video generation. It explicitly decouples motion generation and appearance synthesis to produce kinematically plausible, artistically expressive, and visually coherent dance videos.

The framework consists of two main components:

• Motion Expert: Performs music-to-3D motion generation (SMPL) using a diffusion model with a BiMamba-Transformer architecture.
• Appearance Expert: Synthesizes the final dance video conditioned on the motion and a reference image, ensuring visual identity and spatiotemporal coherence.

📦 Pretrained Weights

This repository stores the pretrained weights used in MACE-Dance, including both expert models and evaluation models.

🗂️ Directory Structure

weight/
├── Evaluation-Appearance/
│   └── Evaluation-Appearance.7z
├── Evaluation-Motion/
│   └── Evaluation-Motion.7z
├── Expert-Appearance/
│   └── Expert-Apprearance.7z
└── Expert-Motion/
    └── Expert-Motion.7z

📘 Description

• 🎭 Expert-Appearance: Pretrained weights for the Appearance Expert (motion-guided video synthesis).
• 🕺 Expert-Motion: Pretrained weights for the Motion Expert (music-to-3D dance motion).
• 📊 Evaluation-Appearance: Weights used for appearance-related evaluation (based on VBench).
• 📈 Evaluation-Motion: Weights used for motion-related evaluation (based on ViTPose).

📝 Notes

• Each subfolder contains a compressed .7z package. Please extract the corresponding file before use.
• Make sure the extracted weights are placed in the expected paths for training, inference, or evaluation as specified in the official code repository.
• Use Expert-* weights for model inference and Evaluation-* weights for metric computation pipelines.

📄 Citation

If you find this project useful, please consider citing the paper:

@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 Jiahong Wu and Chu, Xiangxiang and Liu, Hongyan and He, Jun},
  journal={ACM Transactions on Graphics (SIGGRAPH 2026)},
  year={2026}
}