  Measuring the acoustic characteristics of a space is often done by capturing
its impulse response (IR), a representation of how a full-range stimulus sound
excites it. This is the first work that generates an IR from a single image,
which we call Image2Reverb. This IR is then applied to other signals using
convolution, simulating the reverberant characteristics of the space shown in
the image. Recording these IRs is both time-intensive and expensive, and often
infeasible for inaccessible locations. We use an end-to-end neural network
architecture to generate plausible audio impulse responses from single images
of acoustic environments. We evaluate our method both by comparisons to ground
truth data and by human expert evaluation. We demonstrate our approach by
generating plausible impulse responses from diverse settings and formats
including well known places, musical halls, rooms in paintings, images from
animations and computer games, synthetic environments generated from text,
panoramic images, and video conference backgrounds.
