In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations
This addresses the generalization issue in 3D pose estimation for real-world applications, but it is incremental as it builds on existing methods with a novel architecture.
The paper tackles the problem of monocular 3D human pose estimation in real-world scenes by proposing a deep learning method that uses explicit 2D features and intermediate 3D representations, achieving state-of-the-art accuracy on challenging in-the-wild data.
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.