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

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 approximately, a symmetric structure. We show that reasoning about illumination allows us to exploit the underlying object symmetry even if ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/tpami.2021.3076536

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Name:
European Commission
Grant:
638009
Publisher:
IEEE
Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence More from this journal
Volume:
45
Issue:
4
Pages:
5268-5281
Publication date:
2021-04-29
Acceptance date:
2021-04-16
DOI:
EISSN:
1939-3539
ISSN:
0162-8828
Language:
English
Keywords:
Pubs id:
1174100
Local pid:
pubs:1174100
Deposit date:
2021-05-04

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