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Unsupervised learning of probably symmetric deformable 3D objects from images in the wild

Alternative title:
Conference paper
Abstract:

We propose a method to learn 3D deformable object categories from raw single-view images, without external supervision. The method is based on an autoencoder that factors each input image into depth, albedo, viewpoint and illumination. In order to disentangle these components without supervision, we use the fact that many object categories have, at least in principle, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if t...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/CVPR42600.2020.00008

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Name:
European Commission
Grant:
638009
Publisher:
Institute of Electrical and Electronics Engineers
Host title:
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Pages:
1-10
Publication date:
2020-08-05
Acceptance date:
2020-02-23
Event title:
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Event location:
Seattle, Washington
Event website:
http://cvpr2020.thecvf.com/
Event start date:
2020-06-14
Event end date:
2020-06-19
DOI:
EISSN:
2575-7075
ISSN:
1063-6919
EISBN:
9781728171685
ISBN:
9781728171692
Language:
English
Keywords:
Pubs id:
1102564
Local pid:
pubs:1102564
Deposit date:
2020-05-01

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