  It has been recently shown that neural networks can recover the geometric
structure of a face from a single given image. A common denominator of most
existing face geometry reconstruction methods is the restriction of the
solution space to some low-dimensional subspace. While such a model
significantly simplifies the reconstruction problem, it is inherently limited
in its expressiveness. As an alternative, we propose an Image-to-Image
translation network that jointly maps the input image to a depth image and a
facial correspondence map. This explicit pixel-based mapping can then be
utilized to provide high quality reconstructions of diverse faces under extreme
expressions, using a purely geometric refinement process. In the spirit of
recent approaches, the network is trained only with synthetic data, and is then
evaluated on in-the-wild facial images. Both qualitative and quantitative
analyses demonstrate the accuracy and the robustness of our approach.
