| title | MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation |
|---|---|
| authors | Kaixing Yang, Jiashu Zhu, Xulong Tang, Ziqiao Peng, Xiangyue Zhang, Puwei Wang, Jiahong Wu, Xiangxiang Chu, Hongyan Liu, Jun He |
| date | 2026-05-07 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2512.18181 |
| affiliation | AMAP-ML |
| upvotes | 49 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | With the rise of online dance-video platforms and rapid advances in AI-generated content (AIGC), music-driven dance generation has emerged as a compelling research direction. Despite substantial progress in related domains such as music-driven 3D dance generation, pose-driven image animation, and audio-driven talking-head synthesis, existing methods cannot be directly adapted to this task. Moreover, the limited studies in this area still struggle to jointly achieve high-quality visual appearance and realistic human motion. Accordingly, we present MACE-Dance, a music-driven dance video generation framework with cascaded Mixture-of-Experts (MoE). The Motion Expert performs music-to-3D motion generation while enforcing kinematic plausibility and artistic expressiveness, whereas the Appearance Expert carries out motion- and reference-conditioned video synthesis, preserving visual identity with spatiotemporal coherence. Specifically, the Motion Expert adopts a diffusion model with a BiMamba-Transformer hybrid architecture and a Guidance-Free Training (GFT) strategy, achieving state-of-the-art (SOTA) performance in 3D dance generation. The Appearance Expert employs a decoupled kinematic-aesthetic fine-tuning strategy, achieving state-of-the-art (SOTA) performance in pose-driven image animation. To better benchmark this task, we curate a large-scale and diverse dataset and design a motion-appearance evaluation protocol. Based on this protocol, MACE-Dance also achieves state-of-the-art performance. Code is available at https://github.com/AMAP-ML/MACE-Dance. |
cite
@misc{yang2026mace,
title={MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation},
author={Kaixing Yang and Jiashu Zhu and Xulong Tang and Ziqiao Peng and Xiangyue Zhang and Puwei Wang and Jiahong Wu and Xiangxiang Chu and Hongyan Liu and Jun He},
year={2026},
eprint={2512.18181},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2512.18181}
}