Novel-View Acoustic Synthesis
Abstract
We introduce the novel-view acoustic synthesis (NVAS) task: given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen target viewpoint? We propose a neural rendering approach: Visually-Guided Acoustic Synthesis (ViGAS) network that learns to synthesize the sound of an arbitrary point in space by analyzing the input audio-visual cues. To benchmark this task, we collect two first-of-their-kind large-scale multi-view audio-visual datasets, one synthetic and one real. We show that our model successfully reasons about the spatial cues and synthesizes faithful audio on both datasets. To our knowledge, this work represents the very first formulation, dataset, and approach to solve the novel-view acoustic synthesis task, which has exciting potential applications ranging from AR/VR to art and design. Unlocked by this work, we believe that the future of novel-view synthesis is in multi-modal learning from videos.
Cite
@article{arxiv.2301.08730,
title = {Novel-View Acoustic Synthesis},
author = {Changan Chen and Alexander Richard and Roman Shapovalov and Vamsi Krishna Ithapu and Natalia Neverova and Kristen Grauman and Andrea Vedaldi},
journal= {arXiv preprint arXiv:2301.08730},
year = {2023}
}
Comments
Accepted at CVPR 2023. Project page: https://vision.cs.utexas.edu/projects/nvas