@inproceedings{
bhosale2024avgs,
title={{AV}-{GS}: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis},
author={Swapnil Bhosale and Haosen Yang and Diptesh Kanojia and Jiankang Deng and Xiatian Zhu},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=4U18ZoRXTD}
}
Novel view acoustic synthesis (NVAS) aims to render binaural audio at any target viewpoint, given a mono audio emitted by a sound source at a 3D scene. Existing methods have proposed NeRF-based implicit models to exploit visual cues as a condition for synthesizing binaural audio. However, in addition to low efficiency originating from heavy NeRF rendering, these methods all have a limited ability of characterizing the entire scene environment such as room geometry, material properties, and the spatial relation between the listener and sound source. To address these issues, we propose a novel Audio-Visual Gaussian Splatting (AV-GS) model. To obtain a material-aware and geometry-aware condition for audio synthesis, we learn an explicit point-based scene representation with audio-guidance parameters on locally initialized Gaussian points, taking into account the space relation from the listener and sound source. To make the visual scene model audio adaptive, we propose a point densification and pruning strategy to optimally distribute the Gaussian points, with the per-point contribution in sound propagation (e.g., more points needed for texture-less wall surfaces as they affect sound path diversion). Extensive experiments validate the superiority of our AV-GS over existing alternatives on the real-world RWAS and simulation-based SoundSpaces datasets. Project page: \url{https://surrey-uplab.github.io/research/avgs/}
AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis
Swapnil Bhosale Haosen Yang Diptesh Kanojia Jiankang Deng Xiatian Zhu