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We introduce a method for generating realistic pedestrian trajectories andfull-body animations that can be controlled to meet user-defined goals. We drawon recent advances in guided diffusion modeling to achieve test-timecontrollability of trajectories, which is normally only associated withrule-based systems. Our guided diffusion model allows users to constraintrajectories through target waypoints, speed, and specified social groups whileaccounting for the surrounding environment context. This trajectory diffusionmodel is integrated with a novel physics-based humanoid controller to form aclosed-loop, full-body pedestrian animation system capable of placing largecrowds in a simulated environment with varying terrains. We further proposeutilizing the value function learned during RL training of the animationcontroller to guide diffusion to produce trajectories better suited forparticular scenarios such as collision avoidance and traversing uneven terrain.Video results are available on the project page athttps://nv-tlabs.github.io/trace-pace .

Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion