In this talk, I will present my understanding on 3D face reconstruction, modelling and applications from a deep learning perspective. In the first part of my talk, I will discuss the relationship between representations (point clouds, meshes, etc) and network layers (CNN, GCN, etc) on face reconstruction task, then present my ECCV work PRN which proposed a new representation to help achieve state-of-the-art performance on face reconstruction and dense alignment tasks. I will also introduce my open source project face3d that provides examples for generating different 3D face representations. In the second part of the talk, I will talk some publications in integrating 3D techniques into deep networks, then introduce my upcoming work which implements this. In the third part, I will present how related tasks could promote each other in deep learning, including face recognition for face reconstruction task and face reconstruction for face anti-spoofing task. Finally, with such understanding of these three parts, I will present my plans on 3D face modelling and applications.
Biography: Yao Feng is a master student from Shanghai Jiao Tong University, under the supervision of Xi Zhou and Yanfeng Wang. She worked as a research intern at CIGIT, Chinese Academy of Sciences (2015), Horizon Robotics (2016) and CloudWalk Technology (2016-2018), she will have a research internship at Yaser Sheikh’s team in Facebook Reality Labs Pittsburgh from April 2019. Yao Feng has done many projects contributing to open source community. In GitHub, she gained more than 3.6k stars, repositories including the released code of her ECCV 2018 publication “Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network” PRNet (2.3k+ stars), implementation of classical generative adversarial nets GAN(900+ stars), and a 3D face library face3d(300+ stars). Her research interests include 3D face reconstruction, modelling and applications.