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Face Normals "in-the- wild" using Fully Convolutional Networks

Trigeorgis, G; Snape, P; Kokkinos, I; Zafeiriou, S; (2017) Face Normals "in-the- wild" using Fully Convolutional Networks. In: (Proceedings) 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (pp. pp. 340-349). IEEE Green open access

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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.

Type: Proceedings paper
Title: Face Normals "in-the- wild" using Fully Convolutional Networks
Event: 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Location: Honolulu, HI
Dates: 21 July 2017 - 26 July 2017
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/CVPR.2017.44
Publisher version: https://doi.org/10.1109/CVPR.2017.44
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Engineering, Electrical & Electronic, Computer Science, Engineering, RECOGNITION, SHAPE, DATABASE, ILLUMINATION, IMAGES, MODELS
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10060984
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