Wan-Dancer is a hierarchical framework that generates long-duration (over one minute), high-definition (720p/30fps), rhythmically synchronized dance videos directly from music by decoupling the process into global keyframe planning and local temporal refinement.
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Apache-2.0
2026-07-17
It overcomes the traditional 20-second duration barrier of diffusion models for music-to-dance generation, achieving minute-scale coherent videos with high temporal stability and supporting five distinct dance genres.
💜 Project    ｜    🖥️ GitHub    |   🤖 MS Space   |   🤖 MS Model   |   🤗 HF Model   |    📑 Paper   
Generating long-duration, high-quality, rhythmic dance videos from music with global structure and temporal continuity
Generating long-duration, high-definition, and rhythmically synchronized dance videos directly from music remains a significant challenge, primarily due to the temporal constraints of current diffusion models, which typically fail beyond 20 seconds. Existing approaches, whether they rely on intermediate 3D skeletons or on end-to-end video synthesis, suffer from temporal drift, identity inconsistency, and repetitive motion patterns when extended to longer horizons. To address these limitations, we propose a novel hierarchical framework for minute-scale coherent music-to-dance generation. Our method decouples the process into global keyframe planning and local temporal refinement, leveraging full-track musical context to ensure long-range coherence. Key innovations include dynamic frame rate adaptation via time-mapped RoPE embeddings for precise alignment, an optical-flow-based loss function to enhance motion continuity, and motion-speed control to preserve high-fidelity details during rapid movements. Extensive experiments demonstrate that our framework surpasses the conventional duration barrier, generating stable, 720p/30fps videos exceeding one minute with superior temporal stability. Furthermore, the model exhibits robust versatility across five distinct dance genres, conditioned on both audio and textual prompts, establishing a new state-of-the-art in coherent, long-form dance video synthesis.
We tested the code on:
cd /path/to/Wan-Dancer
python -m venv venv_wan_dancer
source venv_wan_dancer/bin/activate
# Install package in editable mode
pip install -e .
# Install additional and specific versions dependencies
pip install moviepy loguru librosa
pip install https://mirrors.aliyun.com/pytorch-wheels/cu124/torch-2.6.0+cu124-cp310-cp310-linux_x86_64.whl
pip install torchvision==0.21.0
pip install diffusers==0.34.0
pip install yunchang==0.5.0
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