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Convolutional Neural Network based approaches for monocular 3D human poseestimation usually require a large amount of training images with 3D poseannotations. While it is feasible to provide 2D joint annotations for largecorpora of in-the-wild images with humans, providing accurate 3D annotations tosuch in-the-wild corpora is hardly feasible in practice. Most existing 3Dlabelled data sets are either synthetically created or feature in-studioimages. 3D pose estimation algorithms trained on such data often have limitedability to generalize to real world scene diversity. We therefore propose a newdeep learning based method for monocular 3D human pose estimation that showshigh accuracy and generalizes better to in-the-wild scenes. It has a networkarchitecture that comprises a new disentangled hidden space encoding ofexplicit 2D and 3D features, and uses supervision by a new learned projectionmodel from predicted 3D pose. Our algorithm can be jointly trained on imagedata with 3D labels and image data with only 2D labels. It achievesstate-of-the-art accuracy on challenging in-the-wild data.

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