Video: https://youtu.be/twH-vfpQrX4
Abstract: In this talk, I will cover three works related to 3D scene understanding. I will start with the design of a scene analysis network whose design is inspired by classical robust / non-convex optimization for permutation equivariant learning (i.e. wide-baseline stereo, robust parametric fitting, and robust classification). I will then introduce a representation of digital humans based on pose-conditioned implicit functions, which, in contrast to classical mesh-based representations (SMPL) can be learnt end-to-end, as well as illustrate their immediate applicability in classical downstream dense tracking applications. I will conclude by introducing a hybrid implicit/explicit differentiable representation of 3D geometry, one that is easy to train (as it uses implicit functions), yet generates polygonal meshes without the need for iso-surface extraction (e.g. marching cubes).
Bio: Andrea Tagliasacchi is a senior research scientist in the Brain/Toronto office headed by Geoffrey Hinton, where he leads the inverse graphics research pillar. With the exception of a brief hiatus at Google/Stadia, he spent most of 2018 as a visiting faculty in Daydream Augmented Perception (Shahram Izadi) working on 4D data (capture, tracking, compression, modeling, simulation of geometry). Before joining Google, he was an assistant professor at the University of Victoria (2015-2017), where he held the industrial research chair in 3D sensing. His alma mater includes EPFL (postdoc, Mark Pauly), SFU (PhD, Richard Zhang and Daniel Cohen-or, NSERC Alexander Graham Bell fellow), and Politecnico di Milano (gold medalist). He is also an adjunct faculty at the University of Toronto. http://gfx.uvic.ca/people/ataiya/