  We present a fully automatic system that takes a 3D scene and generates
plausible 3D human bodies that are posed naturally in that 3D scene. Given a 3D
scene without people, humans can easily imagine how people could interact with
the scene and the objects in it. However, this is a challenging task for a
computer as solving it requires that (1) the generated human bodies to be
semantically plausible within the 3D environment (e.g. people sitting on the
sofa or cooking near the stove), and (2) the generated human-scene interaction
to be physically feasible such that the human body and scene do not
interpenetrate while, at the same time, body-scene contact supports physical
interactions. To that end, we make use of the surface-based 3D human model
SMPL-X. We first train a conditional variational autoencoder to predict
semantically plausible 3D human poses conditioned on latent scene
representations, then we further refine the generated 3D bodies using scene
constraints to enforce feasible physical interaction. We show that our approach
is able to synthesize realistic and expressive 3D human bodies that naturally
interact with 3D environment. We perform extensive experiments demonstrating
that our generative framework compares favorably with existing methods, both
qualitatively and quantitatively. We believe that our scene-conditioned 3D
human generation pipeline will be useful for numerous applications; e.g. to
generate training data for human pose estimation, in video games and in VR/AR.
Our project page for data and code can be seen at:
\url{https://vlg.inf.ethz.ch/projects/PSI/}.
