  Human pose estimation is a major computer vision problem with applications
ranging from augmented reality and video capture to surveillance and movement
tracking. In the medical context, the latter may be an important biomarker for
neurological impairments in infants. Whilst many methods exist, their
application has been limited by the need for well annotated large datasets and
the inability to generalize to humans of different shapes and body
compositions, e.g. children and infants. In this paper we present a novel
method for learning pose estimators for human adults and infants in an
unsupervised fashion. We approach this as a learnable template matching problem
facilitated by deep feature extractors. Human-interpretable landmarks are
estimated by transforming a template consisting of predefined body parts that
are characterized by 2D Gaussian distributions. Enforcing a connectivity prior
guides our model to meaningful human shape representations. We demonstrate the
effectiveness of our approach on two different datasets including adults and
infants.
