  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 training 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-/weakly-supervised methods.
