Learning Dense Facial Correspondences in Unconstrained Images


We present a minimalists but effective neural network that computes dense facial correspondences in highly unconstrained RGB images. Our network learns a per-pixel flow and a matchability mask between 2D input photographs of a person and the projection of a textured 3D face model. To train such a network, we generate a massive dataset of synthetic faces… (More)
DOI: 10.1109/ICCV.2017.506


11 Figures and Tables

Slides referencing similar topics