Abstract:

Generative models have empowered human creators to visualize their imaginations without artistic skills and labor. A prominent example is large-scale text-to-image generation models. However, these models often are difficult to control and do not respect 3D perspective geometry and temporal consistency of videos. In this talk, I will showcase several of our recent efforts to improve controllability for image, video, 3D generation, and editing. Specifically, I will talk about how we improve semantic control for 2D image generations, generate realistic textures from reference images for 3D objects, and synthesize novel views, lighting, and weather for 3D scenes.

Bio:

Jia-Bin Huang is a Capital One-endowed Associate Professor of Computer Science at the University of Maryland College Park. Before coming to UMD, Huang was a research scientist at Meta Reality Labs. Before that, he was an Assistant Professor of Electrical and Computer Engineering at Virginia Tech. Huang received his Ph.D. from the University of Illinois, Urbana-Champaign (UIUC) in 2016. His research interests include 3D computer vision, generative models, and computational photography. Huang is the recipient of the Thomas & Margaret Huang Award, NSF CRII award, faculty award from Samsung, Google, 3M, Qualcomm, and a Google Research Scholar Award.

Homepage:  https://jbhuang0604.github.io/