Learning to Re-create Reality in 3D

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’s research interests lie in the intersection of computer vision and graphics, where he studies realistic reconstruction and generation of 3D scenes using neural and physical representations. Generations of large-scale, dynamic, and interactive 3D scenes are his current primary targets. His group explores 3D vision and graphics, and their applications to robotics, medical imaging, and scientific problems. He is the lead author of DeepSDF, which introduced neural implicit representation and made a profound impact on 3D computer vision. Before coming to Michigan, he was a postdoctoral researcher at Stanford University and a Ph.D. student at U of Washington, supported by Apple AI/ML Fellowship. He did his undergraduate studies at Caltech.