  Human movement is goal-directed and influenced by the spatial layout of the
objects in the scene. To plan future human motion, it is crucial to perceive
the environment -- imagine how hard it is to navigate a new room with lights
off. Existing works on predicting human motion do not pay attention to the
scene context and thus struggle in long-term prediction. In this work, we
propose a novel three-stage framework that exploits scene context to tackle
this task. Given a single scene image and 2D pose histories, our method first
samples multiple human motion goals, then plans 3D human paths towards each
goal, and finally predicts 3D human pose sequences following each path. For
stable training and rigorous evaluation, we contribute a diverse synthetic
dataset with clean annotations. In both synthetic and real datasets, our method
shows consistent quantitative and qualitative improvements over existing
methods.
