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Procrustean Regression Networks: Learning 3D Structure of Non-rigid Objects from 2D Annotations

Authors
Park, SungheonLee, MinsikKwak, Nojun
Issue Date
Aug-2020
Publisher
Springer Science and Business Media Deutschland GmbH
Citation
Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX, pp 1 - 18
Pages
18
Indexed
SCOPUS
Journal Title
Computer Vision – ECCV 2020 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIX
Start Page
1
End Page
18
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1512
DOI
10.1007/978-3-030-58526-6_1
Abstract
We propose a novel framework for training neural networks which is capable of learning 3D information of non-rigid objects when only 2D annotations are available as ground truths. Recently, there have been some approaches that incorporate the problem setting of non-rigid structure-from-motion (NRSfM) into deep learning to learn 3D structure reconstruction. The most important difficulty of NRSfM is to estimate both the rotation and deformation at the same time, and previous works handle this by regressing both of them. In this paper, we resolve this difficulty by proposing a loss function wherein the suitable rotation is automatically determined. Trained with the cost function consisting of the reprojection error and the low-rank term of aligned shapes, the network learns the 3D structures of such objects as human skeletons and faces during the training, whereas the testing is done in a single-frame basis. The proposed method can handle inputs with missing entries and experimental results validate that the proposed framework shows superior reconstruction performance to the state-of-the-art method on the Human 3.6M, 300-VW, and SURREAL datasets, even though the underlying network structure is very simple. © 2020, Springer Nature Switzerland AG.
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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