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

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

Authors

Aaron S. Jackson

Adrian Bulat

Vasileios Argyriou

Georgios Tzimiropoulos



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). Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression. In Proceedings - 2017 IEEE International Conference on Computer Vision (1031-1039). https://doi.org/10.1109/ICCV.2017.117

Conference Name International Conference on Computer Vision (ICCV17)
Conference Location Venice, Italy
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 Mar 28, 2024
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

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