Towards 3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach

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. Unlike previous two stage approaches that train the two sub-networks sequentially and separately, our training is end-to-end and fully exploits the correlation between the 2D pose and depth estimation sub-tasks.…

• NS
  Natural Science Foundation of Shanghai
• NN
  National Natural Science Foundation of China
• SA
  Science and Technology Commission of Shanghai Municipality
• BM
  Beijing Municipal Science and Technology Commission