Learning to Re-create Reality in 3D

CSE & ECE Joint Interview

Zoom link for remote participants, passcode:  839505

Abstract:  Despite its tremendous impact, 2D media, e.g., photos and videos, remain ‘static’ snapshots of the world that don’t allow us to change our viewpoints or interact with the captured scene. In contrast, modeling the world in 3D can enable interactive experiences such as walking around the scene and manipulating objects. However, traditional pipelines of designing 3D media content are time-consuming and require expert knowledge. In my research, I aim to democratize 3D media by building intelligent systems that learn to synthesize realistic 3D content. Toward this goal, I seek answers to fundamental 3D learning problems including 1) how to effectively represent 3D data, 2) how to train 3D generative models from 2D inputs, and 3) how to generate large, compositional scenes. My work has far-reaching implications, as 3D reconstruction and generation technologies have broad applications in many fields including robotics, autonomous driving, and medical imaging.

Bio:  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 the lead author of DeepSDF, which introduced neural implicit representation and made a profound impact on 3D computer vision. 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. More information can be found on his webpage: https://jjparkcv.github.io/.