Unsupervised Domain Adaptation for 3D Human Pose Estimation-Reference-Cited by-同舟云学术

Unsupervised Domain Adaptation for 3D Human Pose Estimation

Published:2019-10-15 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 27th ACM International Conference on Multimedia
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Author:

Zhang Xiheng¹,Wong Yongkang²,Kankanhalli Mohan S.²,Geng Weidong³

Affiliation:

1. Zhejiang University, Hangzhou, China

2. National Univerisity of Singapore, Singapore, Singapore

3. Zhejiang Univerisity, Hangzhou, China

Funder

National Key Research and Development Program of China

National Research Foundation Prime Minister's Office Singapore

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3343031.3351052

Reference60 articles.

1. Mykhaylo Andriluka Leonid Pishchulin Peter Gehler and Bernt Schiele. 2014. 2D human pose estimation: New benchmark and state of the art analysis. In CVPR . 3686--3693. Mykhaylo Andriluka Leonid Pishchulin Peter Gehler and Bernt Schiele. 2014. 2D human pose estimation: New benchmark and state of the art analysis. In CVPR . 3686--3693.

2. Konstantinos Bousmalis George Trigeorgis Nathan Silberman Dilip Krishnan and Dumitru Erhan. 2016. Domain separation networks. In NIPS. 343--351. Konstantinos Bousmalis George Trigeorgis Nathan Silberman Dilip Krishnan and Dumitru Erhan. 2016. Domain separation networks. In NIPS. 343--351.

3. Ching-Hang Chen and Deva Ramanan. 2017. 3D human pose estimation = 2D pose estimation Ching-Hang Chen and Deva Ramanan. 2017. 3D human pose estimation = 2D pose estimation

4. matching. In CVPR. 7035--7043. matching. In CVPR. 7035--7043.

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