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Spring 2018 GRASP Seminar Series: Angjoo Kanazawa, UC Berkeley, “Solving Inverse Graphics Problems in-the-wild”

January 19, 2018 @ 11:00 am - 12:00 pm


True interpretation of an image is one where the latent factors that make up the image such as 3D geometry, reflectance, lighting, camera, as well as the semantic correspondence of objects in the image are known. Such decompositions have many practical applications in graphics/entertainment such as motion capture and AR/VR. Besides, the ability to disentangle the factors of variation is fundamental to general intelligence and self-supervised learning from observations. However, even when the structure of the factors are known (i.e. image rendering equation), this is an extremely challenging problem since inverse problems are fundamentally underconstrained. What makes it even more challenging is that in most cases, ground truth values of these latent factors are not attainable at a large-scale for images in-the-wild.

In this talk I discuss how we might address these challenges for two problems: end-to-end single-view 3D mesh recovery of human bodies and decomposition of unconstrained face images into shape, reflectance and illuminance. Key insights are in setting up a structured auto-encoder that reflects the underlying model of the world and in learning data-driven priors. 


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Angjoo is a postdoc at UC Berkeley advised by Jitendra Malik, Trevor Darrell, and Alexei Efros. Her research interests include computer vision, machine learning, and compute graphics. Her PhD thesis was on 3D understanding of deformable objects like animals and humans from a single image. She obtained her BA at NYU with Rob Fergus and PhD at University of Maryland College Park with David Jacobs. She has spent time at Google X, NEC with Manmohan Chandraker, and Max Planck Institute with Michael Black. Her paper “Learning 3D Deformation of Animals from 2D Images” won the best paper award at Eurographics 2016.


January 19, 2018
11:00 am - 12:00 pm
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