MusicInfuser enables video diffusion models to generate synchronized dance videos

Researchers have developed MusicInfuser, a novel approach that enables pre-trained text-to-video diffusion models to generate high-quality dance videos synchronized with music. This method efficiently adapts existing video diffusion models by employing a layer-wise adaptability criterion, significantly reducing training costs and preserving prior knowledge. MusicInfuser effectively bridges the gap between music and video, producing dynamic and diverse dance movements that respond to audio inputs, and it generalizes well to new music and subjects. AI

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IMPACT Enables more dynamic and synchronized video generation from audio inputs, potentially impacting creative tools and media production.

RANK_REASON This is a research paper describing a novel method for video generation. [lever_c_demoted from research: ic=1 ai=1.0]

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• MusicInfuser
• arXiv
• Susung Hong

• paper
• other