  Large-scale capture of human motion with diverse, complex scenes, while
immensely useful, is often considered prohibitively costly. Meanwhile, human
motion alone contains rich information about the scene they reside in and
interact with. For example, a sitting human suggests the existence of a chair,
and their leg position further implies the chair's pose. In this paper, we
propose to synthesize diverse, semantically reasonable, and physically
plausible scenes based on human motion. Our framework, Scene Synthesis from
HUMan MotiON (SUMMON), includes two steps. It first uses ContactFormer, our
newly introduced contact predictor, to obtain temporally consistent contact
labels from human motion. Based on these predictions, SUMMON then chooses
interacting objects and optimizes physical plausibility losses; it further
populates the scene with objects that do not interact with humans. Experimental
results demonstrate that SUMMON synthesizes feasible, plausible, and diverse
scenes and has the potential to generate extensive human-scene interaction data
for the community.
