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

Source: https://huggingface.co/papers/2512.18181

Abstract

MACE-Dance is a music-driven dance video generation framework that combines cascaded Mixture-of-Experts with diffusion models and specialized training strategies to achieve high-quality visual appearance and realistic human motion.

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 cascadedMixture-of-Experts(MoE). The Motion Expert performs music-to-3D motion generation while enforcingkinematic plausibilityandartistic 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 aBiMamba-Transformerhybrid architecture and aGuidance-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 inpose-driven image animation. To better benchmark this task, we curate a large-scale and diverse dataset and design amotion-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.

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