PhysicsSliders: Learning Physical Controls for Audio Foley Generation

Abstract:Physical properties provide intuitive controls for sounds made by interacting objects. The challenge is to make a generative model respond predictably to these controls, both individually and in combination. We present \method\ (PXS), trained on simulated contact sounds with 15 material, geometry and contact parameters. Two separately trained variants accept direct parameter inputs or numerical captions. PXS controls hardness, size, speed and damping, supports room descriptions and produces spectrally distinct outputs at fixed targets. When controlling damping and size together, specifying thickness improves frequency accuracy and joint target delivery. Measurements from real recordings define effective-damping targets, which simulator inversion maps to generation inputs without real-audio fine-tuning. Across 18 impact classes from five real-audio datasets, PXS-Physical follows these targets with mean grouped Spearman correlation $\rho=0.976$ and class-median error 0.094 dex (a factor of 1.24).

Sound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2610.18597 [cs.SD]

(or arXiv:2610.18597v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2610.18597 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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