We introduce a method for generating realistic pedestrian trajectories and
full-body animations that can be controlled to meet user-defined goals. We draw
on recent advances in guided diffusion modeling to achieve test-time
controllability of trajectories, which is normally only associated with
rule-based systems. Our guided diffusion model allows users to constrain
trajectories through target waypoints, speed, and specified social groups while
accounting for the surrounding environment context. This trajectory diffusion
model is integrated with a novel physics-based humanoid controller to form a
closed-loop, full-body pedestrian animation system capable of placing large
crowds in a simulated environment with varying terrains. We further propose
utilizing the value function learned during RL training of the animation
controller to guide diffusion to produce trajectories better suited for
particular scenarios such as collision avoidance and traversing uneven terrain.
Video results are available on the project page at
this https URL .

利用引导扩散建模，实现自定义目标和环境因素下的真实行人轨迹和全身动画控制，并提出利用 RL 训练的价值函数指导扩散生成更适合特殊场景的轨迹。