Multi-view weakly-supervised 3D human pose estimation via human body segmentation

We propose a multi-view weakly-supervised 3D human pose estimation network. In this network, we fuse multi-view features and generate the final 3D pose, supervising the 3D pose using human body segmentation generated by a human parsing network. Using human body segmentation provides powerful supervision for the network. We further propose the Pose2Seg algorithm to transform 3D pose into simple simulated segmentation for all views, which allows the network to utilize the human body segmentation to supervise the 3D pose generated by the network, without the need for complex rendering processes and estimation of human body shape. We demonstrate the effectiveness of our method on various datasets and compare our method to other state-of-the-art algorithms, showing its advantages in terms of both quality and ability to generalize.