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

Jackson, Aaron S.; Bulat, Adrian; Argyriou, Vasileios; Tzimiropoulos, Georgios

Abstract

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. Code and models will be made available at http://aaronsplace.co.uk.

Citation

Jackson, A. S., Bulat, A., Argyriou, V., & Tzimiropoulos, G. (2017, October). Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression. Presented at International Conference on Computer Vision (ICCV17), Venice, Italy

• Presentation Conference Type: Edited Proceedings
• Conference Name: International Conference on Computer Vision (ICCV17)
• Start Date: Oct 22, 2017
• End Date: Oct 29, 2017
• Acceptance Date: Jul 17, 2017
• Online Publication Date: Dec 25, 2017
• Publication Date: Dec 25, 2017
• Deposit Date: Aug 9, 2017
• Publicly Available Date: Dec 25, 2017
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 2017-October
• Pages: 1031-1039
• Series Title: Proceedings (IEEE International Conference on Computer Vision)
• Series ISSN: 2380-7504
• Book Title: Proceedings - 2017 IEEE International Conference on Computer Vision
• ISBN: 978-1-5386-1033-6
• DOI: https://doi.org/10.1109/ICCV.2017.117
• Public URL: https://nottingham-repository.worktribe.com/output/889472
• Publisher URL: https://ieeexplore.ieee.org/document/8237379
• Related Public URLs: http://iccv2017.thecvf.com/
• Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Published in 2017 IEEE International Conference on Computer Vision ISBN 9781538610329
• Contract Date: Aug 9, 2017