Large pose 3D face reconstruction from a single image via direct volumetric CNN regression
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 correspon...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2017
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| Online Access: | https://eprints.nottingham.ac.uk/44754/ |
| _version_ | 1848796990764220416 |
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| author | Jackson, Aaron S. Bulat, Adrian Argyriou, Vasileios Tzimiropoulos, Georgios |
| author_facet | Jackson, Aaron S. Bulat, Adrian Argyriou, Vasileios Tzimiropoulos, Georgios |
| author_sort | Jackson, Aaron S. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-14T19:56:46Z |
| format | Conference or Workshop Item |
| id | nottingham-44754 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:56:46Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-447542020-05-04T19:14:01Z https://eprints.nottingham.ac.uk/44754/ Large pose 3D face reconstruction from a single image via direct volumetric CNN regression Jackson, Aaron S. Bulat, Adrian Argyriou, Vasileios Tzimiropoulos, Georgios 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 2017-10-24 Conference or Workshop Item PeerReviewed Jackson, Aaron S., Bulat, Adrian, Argyriou, Vasileios and Tzimiropoulos, Georgios (2017) Large pose 3D face reconstruction from a single image via direct volumetric CNN regression. In: International Conference on Computer Vision (ICCV17), 22-29 Oct 2017, Venice, Italy. http://ieeexplore.ieee.org/document/8237379/ |
| spellingShingle | Jackson, Aaron S. Bulat, Adrian Argyriou, Vasileios Tzimiropoulos, Georgios Large pose 3D face reconstruction from a single image via direct volumetric CNN regression |
| title | Large pose 3D face reconstruction from a single image via direct volumetric CNN regression |
| title_full | Large pose 3D face reconstruction from a single image via direct volumetric CNN regression |
| title_fullStr | Large pose 3D face reconstruction from a single image via direct volumetric CNN regression |
| title_full_unstemmed | Large pose 3D face reconstruction from a single image via direct volumetric CNN regression |
| title_short | Large pose 3D face reconstruction from a single image via direct volumetric CNN regression |
| title_sort | large pose 3d face reconstruction from a single image via direct volumetric cnn regression |
| url | https://eprints.nottingham.ac.uk/44754/ https://eprints.nottingham.ac.uk/44754/ |