3D Landmark Model Discovery from a Registered Set of Organic Shapes

Abstract

We present a machine learning framework that automatically generates a model set of landmarks for some class of registered 3D objects: here we use human faces. The aim is to replace heuristically-designed landmark models by something that is learned from training data. The value of this automatically generated model is an expected improvement in robustness and precision of learning-based 3D landmarking systems. Simultaneously, our framework outputs optimal detectors, derived from a prescribed pool of surface descriptors, for each landmark in the model. The model and detectors can then be used as key components of a landmark-localization system for the set of meshes belonging to that object class. Automatic models have some intrinsic advantages; for example, the fact that repetitive shapes are automatically detected and that local surface shapes are ordered by their degree of saliency in a quantitative way. We compare our automatically generated face landmark model with a manually designed model, employed in existing literature.