  The goal of 2D human pose estimation (HPE) is to localize anatomical
landmarks, given an image of a person in a pose. SOTA techniques make use of
thousands of labeled figures (finetuning transformers or training deep CNNs),
acquired using labor-intensive crowdsourcing. On the other hand,
self-supervised methods re-frame the HPE task as a reconstruction problem,
enabling them to leverage the vast amount of unlabeled visual data, though at
the present cost of accuracy. In this work, we explore ways to improve
self-supervised HPE. We (1) analyze the relationship between reconstruction
quality and pose estimation accuracy, (2) develop a model pipeline that
outperforms the baseline which inspired our work, using less than one-third the
amount of training data, and (3) offer a new metric suitable for
self-supervised settings that measures the consistency of predicted body part
length proportions. We show that a combination of well-engineered
reconstruction losses and inductive priors can help coordinate pose learning
alongside reconstruction in a self-supervised paradigm.
