Prof. Dr. Bernhard Egger studies how humans and machines can perceive faces and shapes in general. In particular, he chooses to focus on statistical shape models and the 3D Morphable Models. He is a junior professor at the chair of visual computing at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Before joing FAU he was a postdoc in Josh Tenenbaum‘s Computational Cognitive Science Lab at the Departement of Brain and Cognitive Sciences at MIT and the Center for Brains, Minds and Machines (CBMM) and Polina Golland‘s group at MIT Computer Science & Artificial Intelligence Lab. He did his PhD on facial image annotation and interpretation in unconstrained images in the Graphics and Vision Research Group at the University of Basel. Before his doctorate he obtained his M.Sc. and B.Sc. in Computer Science at the University of Basel and an upper secondary school teaching Diploma at the University of Applied Sciences Northwestern Switzerland.
Inverse Graphics and Perception with Generative Face Models
Human object perception is remarkably robust: Even when confronted with blurred or sheared photographs, or pictures taken under extreme illumination conditions, we can often recognize what we’re seeing and even recover rich three-dimensional structure. This robustness is especially notable when perceiving human faces. How can humans generalize so well to highly distorted images, transformed far beyond the range of natural face images we are normally exposed to? In this talk I will present an Analysis-by-Synthesis approach based on 3D Morphable Models that can generalize well across various distortions. We find that our top-down inverse rendering model better matches human precepts than either an invariance-based account implemented in a deep neural network, or a neural network trained to perform approximate inverse rendering in a feedforward circuit.