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About

Xinchen Yan is a researcher at the intersection of 3D computer vision, human modeling, autonomous driving, and robotic learning. His work spans some of the most challenging problems in embodied AI and simulation, with a particular focus on building scalable, data-driven systems that bridge the gap between virtual and real-world environments. Yan's most influential contribution, "S³: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling" (2021, 68 citations), introduced a unified neural framework for constructing and animating realistic human avatars — a critical capability for virtual reality and robotics simulation. His involvement in the "Waymo Open Dataset: Panoramic Video Panoptic Segmentation" (2022, 55 citations) reflects his significant impact on autonomous driving perception research, contributing large-scale benchmarks that the broader community relies upon. His earlier work on sim-to-real robotic grasping (2019, 29 citations) demonstrated practical solutions for reducing costly real-world data collection through deep point cloud prediction. More recently, Yan has pushed toward generative 3D world modeling with "GINA-3D" (2023, 16 citations), enabling scalable synthesis of implicit neural assets from real sensor data. Across his portfolio, Yan consistently advances the frontier of realistic simulation and embodied intelligence.