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CIS Seminar: “Intrinsic images, lighting and relighting without any labelling”
November 9 at 3:30 PM - 4:30 PM
Intrinsic images are maps of surface properties. A classical problem is to recover an intrinsic image, typically a map of surface lightness,
from an image. The topic has mostly dropped from view, likely for three reasons: training data is mostly synthetic; evaluation is somewhat
uncertain; and clear applications for the resulting albedo are missing. The decline of this topic has a consequence – mostly, we don’t understand and can’t mitigate the effects of lighting.
I will show the results of simple experiments that suggest that very good modern depth and normal predictors are strongly sensitive to lighting — if
you relight a scene in a reasonable way, the reported depth will change. This is intolerable. To fix this problem, we need to be able to produce
many different lightings of the same scene. I will describe a method to do so. First, one learns a method to estimate albedo from images without any labelled training data (which turns out to perform well under traditional evaluations). Then, one forces an image generator to produce many different images that have the same albedo — with care, these are relightings of the same scene. Finally, a GAN inverter allows us to apply the process to real images. I will show some interim results suggesting that learned relightings might genuinely improve estimates of depth, normal and albedo.
David is currently the Fulton-Watson-Copp chair in computer science at U. Illinois at Urbana-Champaign, where I moved from U.C Berkeley,
where I was also full professor. I have occupied the Fulton-Watson-Copp chair in Computer Science at the University of Illinois since 2014. I have
published over 170 papers on computer vision, computer graphics and machine learning. I have served as program co-chair or general co-chair
for vision conferences on many occasions. I received an IEEE technical achievement award for 2005 for my research. I became an IEEE Fellow in 2009, and an ACM Fellow in 2014. My textbook, “Computer Vision: A Modern Approach” (joint with J. Ponce and published b Prentice Hall) was widely adopted as a course text. My recent textbook, “Probability and Statistics for Computer Science”, is in the top quartile of Springer computer
science chapter downloads. A further textbook “Applied Machine Learning” has just appeared in print. I have served two terms as Editor in Chief, IEEE TPAMI. I serve on a number of scientific advisory boards.