# PhysicsSliders: Learning Physical Controls for Audio Foley Generation

arXiv: https://arxiv.org/abs/2610.18597 · HTML: https://mlsanity.com/paper/2610.18597/

## 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.

## Builds on (most cited first, 20 of 20)

- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [Stable Audio Open](https://mlsanity.com/paper/2407.14358/) (2024)
- [TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization](https://mlsanity.com/paper/2412.21037/) (2024)
- [The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objects](https://mlsanity.com/paper/2306.00956/) (2023)
- [Sketch2Sound: Controllable Audio Generation via Time-Varying Signals and Sonic Imitations](https://mlsanity.com/paper/2412.08550/) (2024)
- [Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos](https://mlsanity.com/paper/2303.16897/) (2023)
- [T-FOLEY: A Controllable Waveform-Domain Diffusion Model for Temporal-Event-Guided Foley Sound Synthesis](https://mlsanity.com/paper/2401.09294/) (2024)
- [RealImpact: A Dataset of Impact Sound Fields for Real Objects](https://mlsanity.com/paper/2306.09944/) (2023)
- [FreeSliders: Training-Free, Modality-Agnostic Concept Sliders for Fine-Grained Diffusion Control in Images, Audio, and Video](https://mlsanity.com/paper/2511.00103/) (2025)
- [Learning Control of Neural Sound Effects Synthesis from Physically Inspired Models](https://mlsanity.com/paper/2503.08806/) (2025)
- [SonicGauss: Position-Aware Physical Sound Synthesis for 3D Gaussian Representations](https://mlsanity.com/paper/2507.19835/) (2025)
- [PAVAS: Physics-Aware Video-to-Audio Synthesis](https://mlsanity.com/paper/2512.08282/) (2025)
- [Low-Resource Guidance for Controllable Latent Audio Diffusion](https://mlsanity.com/paper/2603.04366/) (2026)
- [FillGauss: Fine-Grained Filling-Aware Impact Sound Generation for 3D Gaussian Splatting](https://mlsanity.com/paper/2607.17773/) (2026)

## 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).