This is a hybrid event with in-person attendance in Raisler Lounge and virtual attendance on Zoom.

ABSTRACT

Nature presents a captivating confluence of diversity and similarity. In order to make sense of our visual experiences in the world, humans as well as other natural intelligences are innately adept at recognizing the underlying intrinsic patterns, by simply looking at 2D projections of a constantly evolving 3D environment. Designing unsupervised perception systems to do the same is not only key to many AR/VR and robotics applications, but also a cornerstone for understanding visual perception in general. In pursuit of this ultimate goal, this talk will mainly focus on a recent line of effort in learning dynamic 3D objects like animals, simply from casually-recorded, unlabeled Internet images and videos. In addition, I will also briefly discuss a few other relevant works on inverse rendering, visual concept learning and spatial audio synthesis, which, collectively, attempt to explore the various aspects of our experiences in the natural world.