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Talk Abstract:  Despite its tremendous success, 2D media, e.g., photos and videos, remain ‘static’ snapshots of the world. That is, we cannot walk around freely within a video or interact with the people inside. To unlock richer interactive experiences, I build artificial intelligence (AI) to democratize 3D media that have the capabilities to digitally ‘re-render’ and interact with the 3D world. Towards this goal, I aim to enable people to casually ‘capture’ 3D content, as they would take photos and videos. On the other hand, I seek to build generative AI systems that help non-experts in authoring realistic 3D content, such as objects, scenes, and their dynamics. Such faithful reconstruction and synthesis of 3D scenes are challenging due to the complex physics behind the world and the innate ambiguities coming from sparse observations. In this talk, I discuss my recent progress on the fundamental problems towards my vision: 1) representing 3D data with neural networks, 2) training 3D generative models, and 3) combining physics and machine learning to build robust AI systems that can extrapolate beyond what’s observed.

Speaker Biography:  Jeong Joon (JJ) Park is a postdoctoral researcher at Stanford University, working with Professors Leonidas Guibas and Gordon Wetzstein. His main research interests lie in the intersection of computer vision, graphics, and machine learning, where he studies realistic reconstruction and synthesis of 3D scenes using neural and physical representations. He did his PhD in computer science at the University of Washington, Seattle, under the supervision of Professor Steve Seitz, during which he was supported by Apple AI/ML Fellowship. He is fortunate to have worked with great collaborators from his academic institutions and internships with Adobe, Meta, and Apple. Prior to PhD, he received his Bachelor of Science from the California Institute of Technology.