Monocular 3D Human Pose Estimation with Domain Feature Alignment and Self Training

Yan-Hong Zhang, Calvin Ku, Min-Chun Hu, Hung-Kuo Chu

Abstract: Despite great success in 3D monocular human pose estimation, the progress of accurate prediction for unseen poses or complex backgrounds is still limited due to the lack of labeled data. In this paper, we use synthetically generated images with 3D ground truth and unlabelled real data to address this domain gap challenge. Unlike recent works that apply the adversarial loss to their models, we propose a novel domain feature alignment method (DFA) that avoids the disadvantages of unstable training and wrong alignment. In addition, our method leverages self-training with data enhancement to create robust pseudo-labels for real data. The experimental results show the effectiveness of combining self-training with our DFA method on Human 3.6M testing data without using any 3D ground truth real data.