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3D face reconstruction is a fundamental Computer Vision problem ofextraordinary difficulty. Current systems often assume the availability ofmultiple facial images (sometimes from the same subject) as input, and mustaddress a number of methodological challenges such as establishing densecorrespondences across large facial poses, expressions, and non-uniformillumination. In general these methods require complex and inefficientpipelines for model building and fitting. In this work, we propose to addressmany of these limitations by training a Convolutional Neural Network (CNN) onan 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 accuratealignment nor establishes dense correspondence between images, works forarbitrary facial poses and expressions, and can be used to reconstruct thewhole 3D facial geometry (including the non-visible parts of the face)bypassing the construction (during training) and fitting (during testing) of a3D Morphable Model. We achieve this via a simple CNN architecture that performsdirect regression of a volumetric representation of the 3D facial geometry froma single 2D image. We also demonstrate how the related task of facial landmarklocalization can be incorporated into the proposed framework and help improvereconstruction quality, especially for the cases of large poses and facialexpressions. Testing code will be made available online, along with pre-trainedmodels http://aaronsplace.co.uk/papers/jackson2017recon

Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression