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

MPS-Authors
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Habibie,  Ikhsanul
Computer Graphics, MPI for Informatics, Max Planck Society;

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Xu,  Weipeng
Computer Graphics, MPI for Informatics, Max Planck Society;

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Mehta,  Dushyant
Computer Graphics, MPI for Informatics, Max Planck Society;

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Pons-Moll,  Gerard
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

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Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1904.03289.pdf
(Preprint), 4MB

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Citation

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. Retrieved from http://arxiv.org/abs/1904.03289.


Cite as: http://hdl.handle.net/21.11116/0000-0003-F76E-C
Abstract
Convolutional Neural Network based approaches for monocular 3D human pose estimation usually require a large amount of training images with 3D pose annotations. While it is feasible to provide 2D joint annotations for large corpora of in-the-wild images with humans, providing accurate 3D annotations to such in-the-wild corpora is hardly feasible in practice. Most existing 3D labelled data sets are either synthetically created or feature in-studio images. 3D pose estimation algorithms trained on such data often have limited ability to generalize to real world scene diversity. We therefore propose a new deep learning based method for monocular 3D human pose estimation that shows high accuracy and generalizes better to in-the-wild scenes. It has a network architecture that comprises a new disentangled hidden space encoding of explicit 2D and 3D features, and uses supervision by a new learned projection model from predicted 3D pose. Our algorithm can be jointly trained on image data with 3D labels and image data with only 2D labels. It achieves state-of-the-art accuracy on challenging in-the-wild data.