# SonicGauss: Position-Aware Physical Sound Synthesis for 3D Gaussian Representations

arXiv: https://arxiv.org/abs/2507.19835 · HTML: https://mlsanity.com/paper/2507.19835/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2025.

## Built on by (most cited first, 2 of 2)

- [Objects as Audio-Visual Modal Sound Fields](https://mlsanity.com/paper/2608.05145/) (2026)
- [PhysicsSliders: Learning Physical Controls for Audio Foley Generation](https://mlsanity.com/paper/2610.18597/) (2026)

## Abstract (arXiv)

While 3D Gaussian representations (3DGS) have proven effective for modeling the geometry and appearance of objects, their potential for capturing other physical attributes-such as sound-remains largely unexplored. In this paper, we present a novel framework dubbed SonicGauss for synthesizing impact sounds from 3DGS representations by leveraging their inherent geometric and material properties. Specifically, we integrate a diffusion-based sound synthesis model with a PointTransformer-based feature extractor to infer material characteristics and spatial-acoustic correlations directly from Gaussian ellipsoids. Our approach supports spatially varying sound responses conditioned on impact locations and generalizes across a wide range of object categories. Experiments on the ObjectFolder dataset and real-world recordings demonstrate that our method produces realistic, position-aware auditory feedback. The results highlight the framework's robustness and generalization ability, offering a promising step toward bridging 3D visual representations and interactive sound synthesis. Project page: https://chunshi.wang/SonicGauss