  Articulation-centric 2D/3D pose supervision forms the core training objective
in most existing 3D human pose estimation techniques. Except for synthetic
source environments, acquiring such rich supervision for each real target
domain at deployment is highly inconvenient. However, we realize that standard
foreground silhouette estimation techniques (on static camera feeds) remain
unaffected by domain-shifts. Motivated by this, we propose a novel target
adaptation framework that relies only on silhouette supervision to adapt a
source-trained model-based regressor. However, in the absence of any auxiliary
cue (multi-view, depth, or 2D pose), an isolated silhouette loss fails to
provide a reliable pose-specific gradient and requires to be employed in tandem
with a topology-centric loss. To this end, we develop a series of
convolution-friendly spatial transformations in order to disentangle a
topological-skeleton representation from the raw silhouette. Such a design
paves the way to devise a Chamfer-inspired spatial topological-alignment loss
via distance field computation, while effectively avoiding any gradient
hindering spatial-to-pointset mapping. Experimental results demonstrate our
superiority against prior-arts in self-adapting a source trained model to
diverse unlabeled target domains, such as a) in-the-wild datasets, b)
low-resolution image domains, and c) adversarially perturbed image domains (via
UAP).
