Functional Faces: Groupwise Dense Correspondence Using Functional Maps

Chao Zhang, William A. P. Smith, Arnaud Dessein, Nick Pears, Hang Dai; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 5033-5041

In this paper we present a method for computing dense correspondence between a set of 3D face meshes using functional maps. The functional maps paradigm brings with it a number of advantages for face correspondence. First, it allows us to combine various notions of correspondence. We do so by proposing a number of face-specific functions, suited to either within- or between-subject correspondence. Second, we propose a groupwise variant of the method allowing us to compute cycle-consistent functional maps between all faces in a training set. Since functional maps are of much lower dimension than point-to-point correspondences, this is feasible even when the input meshes are very high resolution. Finally, we show how a functional map provides a geometric constraint that can be used to filter feature matches between non-rigidly deforming surfaces.