Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach

Understand

In this paper, we study the task of 3D human pose estimation in the wild.

• This task is challenging due to lack of training data, as existing datasets are either in the wild images with 2D pose or in the lab images with 3D pose.
• We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
• Our network augments a state-of-the-art 2D pose estimation sub-network with a 3D depth regression sub-network.