AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis

• URL: http://arxiv.org/abs/2406.08920v2
• Date: Fri, 14 Jun 2024 06:38:50 GMT
• Title: AV-GS: Learning Material and Geometry Aware Priors for Novel View Acoustic Synthesis
• Authors: Swapnil Bhosale, Haosen Yang, Diptesh Kanojia, Jiankang Deng, Xiatian Zhu,
• Abstract summary: view acoustic synthesis aims to render 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 audio. We propose a novel Audio-Visual Gaussian Splatting (AV-GS) model to characterize the entire scene environment. Experiments validate the superiority of our AV-GS over existing alternatives on the real-world RWAS and simulation-based SoundSpaces datasets.
• Score: 62.33446681243413
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 an audio-guidance parameter 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.

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