  We introduce the visual acoustic matching task, in which an audio clip is
transformed to sound like it was recorded in a target environment. Given an
image of the target environment and a waveform for the source audio, the goal
is to re-synthesize the audio to match the target room acoustics as suggested
by its visible geometry and materials. To address this novel task, we propose a
cross-modal transformer model that uses audio-visual attention to inject visual
properties into the audio and generate realistic audio output. In addition, we
devise a self-supervised training objective that can learn acoustic matching
from in-the-wild Web videos, despite their lack of acoustically mismatched
audio. We demonstrate that our approach successfully translates human speech to
a variety of real-world environments depicted in images, outperforming both
traditional acoustic matching and more heavily supervised baselines.
