Abstract
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