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About

Chenfanfu Jiang is a leading researcher at the intersection of computer graphics, robotics, and physics-based simulation. His work centers on developing computational frameworks that bridge the gap between virtual environments and real-world physical interactions. Jiang’s major contributions include pioneering the use of the Material Point Method (MPM) for robotics, notably introducing a convex formulation that seamlessly integrates MPM with articulated rigid body dynamics for frictional contact—a breakthrough enabling stable, intersection-free simulation. His highly cited paper “Inferring Forces and Learning Human Utilities from Videos” (83 citations) proposes a novel affordance concept that quantifies physical interactions, while “A Virtual Reality Platform for Dynamic Human-Scene Interaction” (59 citations) creates immersive datasets for robot task planning. Jiang also leads the development of Midas, a multi-joint robotics simulator guaranteeing intersection-free contact, and Atlas3D, which generates physically constrained 3D models for fabrication. His recent work on the Bistable Aerial Transformer (BAT) showcases innovative morphing hybrid drones, and X-SLAM advances real-time dense SLAM. With over 200 total citations, Jiang’s research is foundational for autonomous manipulation, simulation-reality transfer, and physically grounded AI.