text: Recovering the latent photorealistic faces from their artistic portraits aids human perception and facial analysis. However, recovering photorealistic faces from stylized portraits while preserving identity is challenging because the fine details of real faces can be distorted or lost in stylized images. In this paper, we present a new Identity-preserving Face Recovery from Portraits (IFRP) method to recover latent photorealistic faces from unaligned stylized portraits. Our IFRP method consists of two components: Style Removal Network (SRN) and Discriminative Network (DN). The SRN is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. By embedding spatial transformer networks into the SRN, our method can compensate for misalignments of stylized faces automatically and output aligned realistic face images. The DN is used to enforce recovered face images to be similar to authentic faces. To ensure the identity preservation, we promote the recovered and ground-truth faces to share similar visual features via a distance measure which compares features of recovered and ground-truth faces extracted from a pre-trained VGG network. Our approach is evaluated on a large-scale synthesized dataset of real and stylized face pairs and outperforms the state-of-the-art methods. In addition, we demonstrate that our method can also recover photorealistic faces from unseen stylized portraits (unavailable in training) as well as original paintings.
  @cite_48: This note presents an extension to the neural artistic style transfer algorithm (). The original algorithm transforms an image to have the style of another given image. For example, a photograph can be transformed to have the style of a famous painting. Here we address a potential shortcoming of the original method: the algorithm transfers the colors of the original painting, which can alter the appearance of the scene in undesirable ways. We describe simple linear methods for transferring style while preserving colors.
  @cite_9: This paper presents a content-aware style transfer algorithm for paintings and photos of similar content using pre-trained neural network, obtaining better results than the previous work. In addition, the numerical experiments show that the style pattern and the content information is not completely separated by neural network.
  @cite_34: Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.
  @cite_11: 
summary: Style transfer is a technique which can render a given content image (input) by incorporating a specific painting style while preserving the contents of input. We distinguish and style transfer methods. The seminal optimization-based work @cite_34 transfers the style of an artistic image to a given photograph. It uses an iterative optimization to generate a target image which is randomly initialized (Gaussian distribution). During the optimization step, the statistics of the neural activations of the target, the content and style images are matched. The idea @cite_34 inspired many follow-up studies. Yin @cite_9 presents a content-aware style transfer method which initializes the optimization algorithm with a content image instead of a random noise. Li and Wand @cite_11 propose a patch-based style transfer method by combining Markov Random Field (MRF) and CNN techniques. The work @cite_48 proposes to transfer the style by using linear models. It preserves colors of content images by matching color histograms.