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  In the Wild Human Pose Estimation using Explicit 2D Features and Intermediate 3D Representations

Habibie, I., Xu, W., Mehta, D., Pons-Moll, G., & Theobalt, C. (2019). In the Wild Human Pose Estimation using Explicit 2D Features and Intermediate 3D Representations. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10897-10906). Piscataway, NJ: IEEE. doi:10.1109/CVPR.2019.01116.

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Genre: Conference Paper
Latex : In the Wild Human Pose Estimation using Explicit {2D} Features and Intermediate {3D} Representations

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1904.03289 (Preprint), 4MB
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1904.03289
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© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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 Creators:
Habibie, Ikhsanul1, Author           
Xu, Weipeng1, Author           
Mehta, Dushyant1, Author           
Pons-Moll, Gerard2, Author           
Theobalt, Christian1, Author           
Affiliations:
1Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              
2Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              

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Language(s): eng - English
 Dates: 20192019
 Publication Status: Published online
 Pages: 12 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: habibieCVPR19
DOI: 10.1109/CVPR.2019.01116
 Degree: -

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Title: 32nd IEEE Conference on Computer Vision and Pattern Recognition
Place of Event: Long Beach, CA, USA
Start-/End Date: 2019-06-15 - 2019-06-20

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Project name : 4DRepLy
Grant ID : 770784
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  Abbreviation : CVPR 2019
Source Genre: Proceedings
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Affiliations:
Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 10897 - 10906 Identifier: ISBN: 978-1-7281-3293-8