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
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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 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: | https://discovery.ucl.ac.uk/id/eprint/10060984 |
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