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

• URL: http://arxiv.org/abs/2512.18181v2
• Date: Mon, 29 Dec 2025 07:11:46 GMT
• 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,
• Abstract summary: MACE-Dance is a music-driven dance video generation framework with cascaded Mixture-of-appearance (MoE)<n>The Motion Expert performs music-to-3D motion generation while enforcing kinematic plausibility and artistic expressiveness.<n>The Appearance Expert carries out motion- and reference-conditioned video synthesis, preserving visual identity withtemporal coherence.
• Score: 20.517753182293095
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Project page: https://macedance.github.io/

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