Learning the nature of generalisation errors in a 3D morphable model

Abstract

In this paper, we present a new method to statistically recover the full 3D shape of a face from a set of sparse feature points. We attribute noise in the feature point positions to generalisation error of the model. We learn the variance of these feature points empirically using out-of-sample data. This allows the shape reconstruction to probabilistically model the way in which feature points deviate from their true position. We are able to reduce the reconstruction error by as much as 12%.