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.

翻译：形态模型因提供简单且富有表现力的形状空间，成为众多以人为中心处理流程的基础。然而，创建此类形态模型既繁琐又昂贵。主要挑战在于建立跨原始扫描数据的密集对应关系，以捕获足够的形状变化。这通常需要结合大量人工干预和非刚性配准来解决。我们观察到，创建形状空间与求解密集对应紧密耦合——虽然需要密集对应来构建形状空间，但富有表现力的形状空间可提供降维空间来约束搜索过程。为此，我们提出BLiSS方法，逐步解决这两个问题。从少量手动配准扫描开始引导流程，我们逐步丰富形状空间，并利用该空间自动建立新未配准扫描的对应关系。BLiSS的关键组件是一个非线性变形模型，它能捕获低维形状空间遗漏的细节，从而渐进式地完善该空间。