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About

Baoxiong Jia is an emerging researcher at the forefront of 3D scene understanding, embodied AI, and robot learning, with work that bridges generative modeling, physical reasoning, and autonomous systems. His most influential contribution, SceneDiffuser, introduced a unified diffusion-based generative model for 3D scene-conditioned generation, optimization, and planning — a framework that is intrinsically scene-aware, physics-based, and goal-oriented, earning over 165 citations since its 2023 publication. This work marked a significant advance over prior approaches by consolidating previously disparate tasks into a single coherent model. Beyond generative modeling, Jia has made notable strides in embodied AI, contributing to the development of generalist agents capable of operating in 3D environments and closed-loop robotic systems powered by large vision-language models such as GPT-4V. His more recent work spans physically plausible 3D object completion with PhysPart, scalable robot learning platforms through RoboVerse, and high-fidelity simulation environments via MetaScenes. Collectively, these contributions reflect a cohesive research vision: enabling intelligent agents to perceive, reason about, and interact with the physical world with greater generalization and physical fidelity — a challenge central to the future of robotics and AI.