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PhysicsSliders: Learning Physical Controls for Audio Foley Generation

arXiv 2610.18597 ↗First posted 13 Oct 2026cs.SD cs.AI cs.LGcited by 0

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Abstract (arXiv)

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 ρ=0.976 and class-median error 0.094 dex (a factor of 1.24).

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 26 references that are in the map. First posted on arXiv 13 Oct 2026.

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References · 20

  1. 2022Classifier-Free Diffusion Guidancecited by 5,847
  2. 2022Flow Matching for Generative Modelingcited by 5,702
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  4. 2022Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentationcited by 719
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  7. 2017Neural Audio Synthesis of Musical Notes with WaveNet Autoencoderscited by 225
  8. 2024Stable Audio Opencited by 197
  9. 2024TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimizationcited by 68
  10. 2023The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objectscited by 41
  11. 2024Sketch2Sound: Controllable Audio Generation via Time-Varying Signals and Sonic Imitationscited by 24
  12. 2023Physics-Driven Diffusion Models for Impact Sound Synthesis from Videoscited by 22
8 more
  1. 2024T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesiscited by 19
  2. 2023RealImpact: A Dataset of Impact Sound Fields for Real Objectscited by 9
  3. 2025FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control in Images, Audio, and Videocited by 4
  4. 2025Learning Control of Neural Sound Effects Synthesis from Physically Inspired Modelscited by 2
  5. 2025SonicGauss: Position-Aware Physical Sound Synthesis for 3D Gaussian Representationscited by 2
  6. 2025PAVAS: Physics-Aware Video-to-Audio Synthesiscited by 2
  7. 2026Low-Resource Guidance for Controllable Latent Audio Diffusioncited by 2
  8. 2026FillGauss: Fine-Grained Filling-Aware Impact Sound Generation for 3D Gaussian Splattingcited by 1

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