  Morphable models are fundamental to numerous human-centered processes as they
offer a simple yet expressive shape space. Creating such morphable models,
however, is both tedious and expensive. The main challenge is establishing
dense correspondences across raw scans that capture sufficient shape variation.
This is often addressed using a mix of significant manual intervention and
non-rigid registration. We observe that creating a shape space and solving for
dense correspondence are tightly coupled -- while dense correspondence is
needed to build shape spaces, an expressive shape space provides a reduced
dimensional space to regularize the search. We introduce BLiSS, a method to
solve both progressively. Starting from a small set of manually registered
scans to bootstrap the process, we enrich the shape space and then use that to
get new unregistered scans into correspondence automatically. The critical
component of BLiSS is a non-linear deformation model that captures details
missed by the low-dimensional shape space, thus allowing progressive enrichment
of the space.
