  Human pose estimation from single images is a challenging problem in computer
vision that requires large amounts of labeled training data to be solved
accurately. Unfortunately, for many human activities (\eg outdoor sports) such
training data does not exist and is hard or even impossible to acquire with
traditional motion capture systems. We propose a self-supervised approach that
learns a single image 3D pose estimator from unlabeled multi-view data. To this
end, we exploit multi-view consistency constraints to disentangle the observed
2D pose into the underlying 3D pose and camera rotation. In contrast to most
existing methods, we do not require calibrated cameras and can therefore learn
from moving cameras. Nevertheless, in the case of a static camera setup, we
present an optional extension to include constant relative camera rotations
over multiple views into our framework. Key to the success are new, unbiased
reconstruction objectives that mix information across views and training
samples. The proposed approach is evaluated on two benchmark datasets
(Human3.6M and MPII-INF-3DHP) and on the in-the-wild SkiPose dataset.
