  In this paper, we use deep neural networks for inverting face sketches to
synthesize photorealistic face images. We first construct a semi-simulated
dataset containing a very large number of computer-generated face sketches with
different styles and corresponding face images by expanding existing
unconstrained face data sets. We then train models achieving state-of-the-art
results on both computer-generated sketches and hand-drawn sketches by
leveraging recent advances in deep learning such as batch normalization, deep
residual learning, perceptual losses and stochastic optimization in combination
with our new dataset. We finally demonstrate potential applications of our
models in fine arts and forensic arts. In contrast to existing patch-based
approaches, our deep-neural-network-based approach can be used for synthesizing
photorealistic face images by inverting face sketches in the wild.
