MusicInfuser: Making Video Diffusion Listen and Dance
Authors: name: Susung Hong, name: Ira Kemelmacher-Shlizerman, name: Brian Curless, name: Steven M. Seitz

We introduce MusicInfuser, an approach for generating high-quality dance
videos that are synchronized to a specified music track. Rather than attempting
to design and train a new multimodal audio-video model, we show how existing
video diffusion models can be adapted to align with musical inputs by
introducing lightweight music-video cross-attention and a low-rank adapter.
Unlike prior work requiring motion capture data, our approach fine-tunes only
on dance videos. MusicInfuser achieves high-quality music-driven video
generation while preserving the flexibility and generative capabilities of the
underlying models. We introduce an evaluation framework using Video-LLMs to
assess multiple dimensions of dance generation quality. The project page and
code are available at https://susunghong.github.io/MusicInfuser.