  3D face reconstruction is a fundamental Computer Vision problem of
extraordinary difficulty. Current systems often assume the availability of
multiple facial images (sometimes from the same subject) as input, and must
address a number of methodological challenges such as establishing dense
correspondences across large facial poses, expressions, and non-uniform
illumination. In general these methods require complex and inefficient
pipelines for model building and fitting. In this work, we propose to address
many of these limitations by training a Convolutional Neural Network (CNN) on
an appropriate dataset consisting of 2D images and 3D facial models or scans.
Our CNN works with just a single 2D facial image, does not require accurate
alignment nor establishes dense correspondence between images, works for
arbitrary facial poses and expressions, and can be used to reconstruct the
whole 3D facial geometry (including the non-visible parts of the face)
bypassing the construction (during training) and fitting (during testing) of a
3D Morphable Model. We achieve this via a simple CNN architecture that performs
direct regression of a volumetric representation of the 3D facial geometry from
a single 2D image. We also demonstrate how the related task of facial landmark
localization can be incorporated into the proposed framework and help improve
reconstruction quality, especially for the cases of large poses and facial
expressions. Testing code will be made available online, along with pre-trained
models http://aaronsplace.co.uk/papers/jackson2017recon
