Although each of the core vision problems poses a variety of engaging challenges, human-centered tasks are particularly intriguing and rewarding from the modeling perspective, as they allow for discovery and analysis of structured patterns, relations and constraints of various nature and levels of abstraction. In this talk, I will focus on our most recent work at Facebook on learning object point correspondences for dense human pose estimation "in the wild" from images and videos, performed in real time. I will also cover some of applications of the DensePose framework to dense pose transfer and avatar synthesis, as well as real time reconstruction of 3D geometry of a human body.
Natalia Neverova is a Research Scientist at Facebook AI Research (FAIR), interested in statistical machine learning and computer vision with emphasis on deep learning and human centered applications. Before coming to FAIR in 2016, she completed her PhD at INSA Lyon (France) and the University of Guelph (Canada) under the guidance of Dr. Christian Wolf and Dr. Graham Taylor, working on deep learning and computer vision models for human motion analysis. She also spent several months as a visiting researcher at Google ATAP.