  Recovering the 3D geometric structure of a face from a single input image is
a challenging active research area in computer vision. In this paper, we
present a novel method for reconstructing 3D heads from a single or multiple
image(s) using a hybrid approach based on deep learning and geometric
techniques. We propose an encoder-decoder network based on the U-net
architecture and trained on synthetic data only. It predicts both pixel-wise
normal vectors and landmarks maps from a single input photo. Landmarks are used
for the pose computation and the initialization of the optimization problem,
which, in turn, reconstructs the 3D head geometry by using a parametric
morphable model and normal vector fields. State-of-the-art results are achieved
through qualitative and quantitative evaluation tests on both single and
multi-view settings. Despite the fact that the model was trained only on
synthetic data, it successfully recovers 3D geometry and precise poses for
real-world images.
