  We propose a straightforward method that simultaneously reconstructs the 3D
facial structure and provides dense alignment. To achieve this, we design a 2D
representation called UV position map which records the 3D shape of a complete
face in UV space, then train a simple Convolutional Neural Network to regress
it from a single 2D image. We also integrate a weight mask into the loss
function during training to improve the performance of the network. Our method
does not rely on any prior face model, and can reconstruct full facial geometry
along with semantic meaning. Meanwhile, our network is very light-weighted and
spends only 9.8ms to process an image, which is extremely faster than previous
works. Experiments on multiple challenging datasets show that our method
surpasses other state-of-the-art methods on both reconstruction and alignment
tasks by a large margin.
