Weakly-Supervised 3D Human Pose Learning via Multi-View Images in the Wild

Umar Iqbal, Pavlo Molchanov, Jan Kautz
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
One major challenge for monocular 3D human pose estimation in-the-wild is the acquisition of training data that contains unconstrained images annotated with accurate 3D poses. In this paper, we address this challenge by proposing a weakly-supervised approach that does not require 3D annotations and learns to estimate 3D poses from unlabeled multi-view data, which can be acquired easily in in-the-wild environments. We propose a novel end-to-end learning framework that enables weakly-supervised
more » ... aining using multi-view consistency. Since multi-view consistency is prone to degenerated solutions, we adopt a 2.5D pose representation and propose a novel objective function that can only be minimized when the predictions of the trained model are consistent and plausible across all camera views. We evaluate our proposed approach on two large scale datasets (Human3.6M and MPII-INF-3DHP) where it achieves state-of-the-art performance among semi-/weaklysupervised methods.
doi:10.1109/cvpr42600.2020.00529 dblp:conf/cvpr/IqbalMK20 fatcat:wmjxjjxm7fce7gc6oe7nj25f2y