  Available 3D human pose estimation approaches leverage different forms of
strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring
synthetic or in-studio domains, acquiring such supervision for each new target
environment is highly inconvenient. To this end, we cast 3D pose learning as a
self-supervised adaptation problem that aims to transfer the task knowledge
from a labeled source domain to a completely unpaired target. We propose to
infer image-to-pose via two explicit mappings viz. image-to-latent and
latent-to-pose where the latter is a pre-learned decoder obtained from a
prior-enforcing generative adversarial auto-encoder. Next, we introduce
relation distillation as a means to align the unpaired cross-modal samples i.e.
the unpaired target videos and unpaired 3D pose sequences. To this end, we
propose a new set of non-local relations in order to characterize long-range
latent pose interactions unlike general contrastive relations where positive
couplings are limited to a local neighborhood structure. Further, we provide an
objective way to quantify non-localness in order to select the most effective
relation set. We evaluate different self-adaptation settings and demonstrate
state-of-the-art 3D human pose estimation performance on standard benchmarks.
