Semantic Synthesis of Pedestrian Locomotion
We present a model for generating 3d articulated pedestrian locomotion in urban scenarios, with synthesis capabilities informed by the 3d scene semantics and geometry. We reformulate pedestrian trajectory forecasting as a structured reinforcement learning (RL) problem. This allows us to naturally combine prior knowledge on collision avoidance, 3d human motion capture and the motion of pedestrians
