  We revisit human motion synthesis, a task useful in various real world
applications, in this paper. Whereas a number of methods have been developed
previously for this task, they are often limited in two aspects: focusing on
the poses while leaving the location movement behind, and ignoring the impact
of the environment on the human motion. In this paper, we propose a new
framework, with the interaction between the scene and the human motion taken
into account. Considering the uncertainty of human motion, we formulate this
task as a generative task, whose objective is to generate plausible human
motion conditioned on both the scene and the human initial position. This
framework factorizes the distribution of human motions into a distribution of
movement trajectories conditioned on scenes and that of body pose dynamics
conditioned on both scenes and trajectories. We further derive a GAN based
learning approach, with discriminators to enforce the compatibility between the
human motion and the contextual scene as well as the 3D to 2D projection
constraints. We assess the effectiveness of the proposed method on two
challenging datasets, which cover both synthetic and real world environments.
