  We apply computer vision pose estimation techniques developed expressly for
the data-scarce infant domain to the study of torticollis, a common condition
in infants for which early identification and treatment is critical.
Specifically, we use a combination of facial landmark and body joint estimation
techniques designed for infants to estimate a range of geometric measures
pertaining to face and upper body symmetry, drawn from an array of sources in
the physical therapy and ophthalmology research literature in torticollis. We
gauge performance with a range of metrics and show that the estimates of most
these geometric measures are successful, yielding strong to very strong
Spearman's $\rho$ correlation with ground truth values. Furthermore, we show
that these estimates, derived from pose estimation neural networks designed for
the infant domain, cleanly outperform estimates derived from more widely known
networks designed for the adult domain
