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

Understand

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.