  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.
