  While 3D human body modeling has received much attention in computer vision,
modeling the acoustic equivalent, i.e. modeling 3D spatial audio produced by
body motion and speech, has fallen short in the community. To close this gap,
we present a model that can generate accurate 3D spatial audio for full human
bodies. The system consumes, as input, audio signals from headset microphones
and body pose, and produces, as output, a 3D sound field surrounding the
transmitter's body, from which spatial audio can be rendered at any arbitrary
position in the 3D space. We collect a first-of-its-kind multimodal dataset of
human bodies, recorded with multiple cameras and a spherical array of 345
microphones. In an empirical evaluation, we demonstrate that our model can
produce accurate body-induced sound fields when trained with a suitable loss.
Dataset and code are available online.
