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.

翻译：在本文中,我们重新审视人类运动合成,这是在现实世界的各种应用中有用的一项任务。虽然以前已经为这项任务制定了一些方法,但通常在两个方面是有限的:在离开位置运动的同时,注重外在的外在,忽视环境对人运动的影响。在本文件中,我们提出一个新的框架,将现场与人运动之间的互动纳入考虑;考虑到人类运动的不确定性,我们将此任务作为一个变异的任务,其目标是在现场和人的初始位置上产生人运动的貌似合理的条件。这个框架将人类运动的分布纳入以场景和身体的动态为条件的运动轨迹分布。我们进一步提出基于GAN的学习方法,使人运动与背景场景之间的兼容性以及3D至2D的预测限制。我们评估了两个具有挑战性的数据集的拟议方法的有效性,这两个数据集既包括合成环境,也包括真实世界环境。