  Synthesizing 3D human motion plays an important role in many graphics
applications as well as understanding human activity. While many efforts have
been made on generating realistic and natural human motion, most approaches
neglect the importance of modeling human-scene interactions and affordance. On
the other hand, affordance reasoning (e.g., standing on the floor or sitting on
the chair) has mainly been studied with static human pose and gestures, and it
has rarely been addressed with human motion. In this paper, we propose to
bridge human motion synthesis and scene affordance reasoning. We present a
hierarchical generative framework to synthesize long-term 3D human motion
conditioning on the 3D scene structure. Building on this framework, we further
enforce multiple geometry constraints between the human mesh and scene point
clouds via optimization to improve realistic synthesis. Our experiments show
significant improvements over previous approaches on generating natural and
physically plausible human motion in a scene.
