Fall 2024 GRASP Seminar Anand Bhattad, Toyota Technological Institute at Chicago

“Are Generative Image Models Physically Grounded?” ABSTRACT Computer vision has transformed from simple edge detection in the 1980s to modern generative models that generate uncannily realistic images: objects are in sensible places, lighting seems realistic, and textures appear accurate. But how do they achieve this understanding of our visual world? Probing their internal representations reveals that these models encode fundamental aspects of physical reality. Within these models, we discovered classical computer vision concepts like intrinsic images — decomposing scenes into color, shape, and lighting — learned without explicit training. These discoveries allow us to manipulate real photographs in physically plausible ways. However, we also find surprising gaps in their understanding, such as their limitation of replicating principles of projective geometry, which provides reliable signatures for detecting generated images. This talk explores what knowledge emerges within generative image models, revealing their strengths and weaknesses. I will discuss how these insights drive new applications and open challenges, pushing us closer to building generative models grounded in the physical world. Presenter Anand Bhattad is a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed academic computer science graduate institute located on the University of Chicago campus. He is also a visiting scholar at UC Berkeley, hosted by Alexei (Alyosha) Efros. Earlier, he completed his PhD in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC) working with David Forsyth. His research interests include computer vision, graphics, and computational photography. He is currently serving as an Area Chair at CVPR 2025 and WACV 2025 and has been awarded Outstanding Reviewer at ICCV 2023 and Emergency Outstanding Reviewer at CVPR 2021. He has led the organization of community building workshops at CVPR including “Scholars and Big Models: How Can Academics Adapt” at CVPR 2023 and “CV 20/20: A Retrospective Vision” at CVPR 2024.