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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-superviseddoi:10.1109/cvpr42600.2020.00529 dblp:conf/cvpr/IqbalMK20 fatcat:wmjxjjxm7fce7gc6oe7nj25f2y