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arXiv · 2601.15872

PF-D2M: A Pose-free Diffusion Model for Universal Dance-to-Music Generation

Jaekwon Im·Natalia Polouliakh·Taketo Akama
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Abstract

Dance-to-music generation aims to generate music that is aligned with dance movements. Existing approaches typically rely on body motion features extracted from a single human dancer and limited dance-to-music datasets, which restrict their performance and applicability to real-world scenarios involving multiple dancers and non-human dancers. In this paper, we propose PF-D2M, a universal diffusion-based dance-to-music generation model that incorporates visual features extracted from dance videos. PF-D2M is trained with a progressive training strategy that effectively addresses data scarcity and generalization challenges. Both objective and subjective evaluations show that PF-D2M achieves state-of-the-art performance in dance-music alignment and music quality.

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BibTeXRIS

Jaekwon Im, Natalia Polouliakh, Taketo Akama. 2026-01-22. PF-D2M: A Pose-free Diffusion Model for Universal Dance-to-Music Generation. https://arxiv.org/abs/2601.15872

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