Numerous style transfer methods which produce artistic styles of portraits
have been proposed to date. However, the inverse problem of converting the
stylized portraits back into realistic faces is yet to be investigated
thoroughly. Reverting an artistic portrait to its original photo-realistic face
image has potential to facilitate human perception and identity analysis. In
this paper, we propose a novel Face Destylization Neural Network (FDNN) to
restore the latent photo-realistic faces from the stylized ones. We develop a
Style Removal Network composed of convolutional, fully-connected and
deconvolutional layers. The convolutional layers are designed to extract facial
components from stylized face images. Consecutively, the fully-connected layer
transfers the extracted feature maps of stylized images into the corresponding
feature maps of real faces and the deconvolutional layers generate real faces
from the transferred feature maps. To enforce the destylized faces to be
similar to authentic face images, we employ a discriminative network, which
consists of convolutional and fully connected layers. We demonstrate the
effectiveness of our network by conducting experiments on an extensive set of
synthetic images. Furthermore, we illustrate our network can recover faces from
stylized portraits and real paintings for which the stylized data was
unavailable during the training phase.

本文提出了一种人脸去艺术化的神经网络方法，可以从艺术化图像中恢复出潜在的真实人脸图像，并且证明了该方法的高效性以及可以处理未曾接触过的艺术化图像。