  A long-standing goal in computer vision is to capture, model, and
realistically synthesize human behavior. Specifically, by learning from data,
our goal is to enable virtual humans to navigate within cluttered indoor scenes
and naturally interact with objects. Such embodied behavior has applications in
virtual reality, computer games, and robotics, while synthesized behavior can
be used as a source of training data. This is challenging because real human
motion is diverse and adapts to the scene. For example, a person can sit or lie
on a sofa in many places and with varying styles. It is necessary to model this
diversity when synthesizing virtual humans that realistically perform
human-scene interactions. We present a novel data-driven, stochastic motion
synthesis method that models different styles of performing a given action with
a target object. Our method, called SAMP, for Scene-Aware Motion Prediction,
generalizes to target objects of various geometries while enabling the
character to navigate in cluttered scenes. To train our method, we collected
MoCap data covering various sitting, lying down, walking, and running styles.
We demonstrate our method on complex indoor scenes and achieve superior
performance compared to existing solutions. Our code and data are available for
research at https://samp.is.tue.mpg.de.
