Ye's research aims to create next-generation perception systems capable of accurately modeling the physical world and reasoning about the behavior and interaction of various agents (e.g., humans, robots, and vehicles). Towards this goal, Ye has leveraged physics simulation, reinforcement learning, and generative models to solve computer vision problems such as human pose estimation, human motion synthesis, and multi-agent trajectory forecasting.