BLiSS: Bootstrapped Linear Shape Space
This addresses the tedious and expensive process of creating morphable models for human-centered applications, offering an incremental improvement over manual and non-rigid registration methods.
The paper tackles the challenge of creating morphable models by introducing BLiSS, a method that progressively builds shape spaces and solves dense correspondence automatically, starting from a small set of manually registered scans to enrich the space and register new scans.
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.