Face Normals "In-The-Wild" Using Fully Convolutional Networks

George Trigeorgis, Patrick Snape, Iasonas Kokkinos, Stefanos Zafeiriou; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 38-47


In this work we pursue a data-driven approach to the problem of estimating surface normals from a single intensity image, focusing in particular on human faces. We introduce new methods to exploit the currently available facial databases for dataset construction and tailor a deep convolutional neural network to the task of estimating facial surface normals `in-the-wild'. We train a fully convolutional network that can accurately recover facial normals from images including a challenging variety of expressions and facial poses. We compare against state-of-the-art face Shape-from-Shading and 3D reconstruction techniques and show that the proposed network can recover substantially more accurate and realistic normals. Furthermore, in contrast to other existing face-specific surface recovery methods, we do not require the solving of an explicit alignment step due to the fully convolutional nature of our network.

Related Material

author = {Trigeorgis, George and Snape, Patrick and Kokkinos, Iasonas and Zafeiriou, Stefanos},
title = {Face Normals "In-The-Wild" Using Fully Convolutional Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}