  Can machines recording an audio-visual scene produce realistic, matching
audio-visual experiences at novel positions and novel view directions? We
answer it by studying a new task -- real-world audio-visual scene synthesis --
and a first-of-its-kind NeRF-based approach for multimodal learning.
Concretely, given a video recording of an audio-visual scene, the task is to
synthesize new videos with spatial audios along arbitrary novel camera
trajectories in that scene. We propose an acoustic-aware audio generation
module that integrates prior knowledge of audio propagation into NeRF, in which
we implicitly associate audio generation with the 3D geometry and material
properties of a visual environment. Furthermore, we present a coordinate
transformation module that expresses a view direction relative to the sound
source, enabling the model to learn sound source-centric acoustic fields. To
facilitate the study of this new task, we collect a high-quality Real-World
Audio-Visual Scene (RWAVS) dataset. We demonstrate the advantages of our method
on this real-world dataset and the simulation-based SoundSpaces dataset.
