Fitting a 3D Morphable Model to Edges: A Comparison Between Hard and Soft Correspondences
This work addresses the specific problem of 3D face model fitting for computer vision applications, presenting an incremental improvement over existing methods.
The authors tackled the problem of automatically fitting a 3D morphable model to single face images under arbitrary pose and lighting by using geometric features like edges and landmarks, and they demonstrated that their method based on hard correspondences is superior to previous soft correspondence approaches.
We propose a fully automatic method for fitting a 3D morphable model to single face images in arbitrary pose and lighting. Our approach relies on geometric features (edges and landmarks) and, inspired by the iterated closest point algorithm, is based on computing hard correspondences between model vertices and edge pixels. We demonstrate that this is superior to previous work that uses soft correspondences to form an edge-derived cost surface that is minimised by nonlinear optimisation.