  Current state-of-the-art methods cast monocular 3D human pose estimation as a
learning problem by training neural networks on large data sets of images and
corresponding skeleton poses. In contrast, we propose an approach that can
exploit small annotated data sets by fine-tuning networks pre-trained via
self-supervised learning on (large) unlabeled data sets. To drive such networks
towards supporting 3D pose estimation during the pre-training step, we
introduce a novel self-supervised feature learning task designed to focus on
the 3D structure in an image. We exploit images extracted from videos captured
with a multi-view camera system. The task is to classify whether two images
depict two views of the same scene up to a rigid transformation. In a
multi-view data set, where objects deform in a non-rigid manner, a rigid
transformation occurs only between two views taken at the exact same time,
i.e., when they are synchronized. We demonstrate the effectiveness of the
synchronization task on the Human3.6M data set and achieve state-of-the-art
results in 3D human pose estimation.
