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  IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction

Shimada, S., Golyanik, V., Theobalt, C., & Stricker, D. (2019). IsMo-GAN: Adversarial Learning for Monocular Non-Rigid 3D Reconstruction. In IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Computer Vision Foundation.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0005-9410-1 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-9411-0
Genre: Conference Paper
Latex : {IsMo-GAN}: {A}dversarial Learning for Monocular Non-Rigid {3D} Reconstruction

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Shimada_IsMo-GAN_Adversarial_Learning_for_Monocular_Non-Rigid_3D_Reconstruction_CVPRW_2019_paper.pdf (Preprint), 2MB
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These CVPR 2019 workshop papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.
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 Creators:
Shimada, Soshi1, Author
Golyanik, Vladislav2, Author           
Theobalt, Christian2, Author           
Stricker, Didier1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              

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Language(s): eng - English
 Dates: 2019
 Publication Status: Published online
 Pages: 10 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Shimada_2019
 Degree: -

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Title: Photogrammetric Computer Vision Workshop
Place of Event: Long Beach, CA, USA
Start-/End Date: 2019-06-17 - 2019-06-17

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Title: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
  Abbreviation : CVPRW 2019
  Subtitle : Proceedings
  Other : PCV 19
  Other : PCV 2019
Source Genre: Proceedings
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Publ. Info: Computer Vision Foundation
Pages: 10 p. Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -