Recently, image-to-image translation has been made much progress owing to the success of conditional Generative Adversarial Networks (cGANs). However, it's still very challenging for translation tasks with the requirement of high-level visual information conversion, such as photo-to-caricature translation that requires satire, exaggeration, lifelikeness and artistry. We present an approach for learning to translate faces in the wild from the source photo domain to the target caricature domain with different styles, which can also be used for other high-level image-to-image translation tasks. In order to capture global structure with local statistics while translation, we design a dual pathway model of cGAN with one global discriminator and one patch discriminator. Beyond standard convolution (Conv), we propose a new parallel convolution (ParConv) to construct Parallel Convolutional Neural Networks (ParCNNs) for both global and patch discriminators, which can combine the information from previous layer with the current layer. For generator, we provide three more extra losses in association with adversarial loss to constrain consistency for generated output itself and with the target. Also the style can be controlled by the input style info vector. Experiments on photo-to-caricature translation of faces in the wild show considerable performance gain of our proposed method over state-of-the-art translation methods as well as its potential real applications.
@cite_14: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to ½ everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
@cite_36: Emotional facial expressions play a critical role in theories of emotion and figure prominently in research on almost every aspect of emotion. This article provides a background for a new database of basic emotional expressions. The goal in creating this set was to provide high quality photographs of genuine facial expressions. Thus, after proper training, participants were inclined to express “felt” emotions. The novel approach taken in this study was also used to establish whether a given expression was perceived as intended by untrained judges. The judgment task for perceivers was designed to be sensitive to subtle changes in meaning caused by the way an emotional display was evoked and expressed. Consequently, this allowed us to measure the purity and intensity of emotional displays, which are parameters that validation methods used by other researchers do not capture. The final set is comprised of those pictures that received the highest recognition marks (e.g. accuracy with intended display) from independent judges, totaling 210 high quality photographs of 30 individuals. Descriptions of the accuracy, intensity, and purity of displayed emotion as well as FACS AU’s codes are provided for each picture. Given the unique methodology applied to gathering and validating this set of pictures, it may be a useful tool for research using face stimuli. The Warsaw Set of Emotional Facial Expression Pictures (WSEFEP) is freely accessible to the scientific community for noncommercial use by request at www.emotional-face.org.
@cite_29: 
@cite_6: We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given function f, which accepts inputs in either domains, would remain unchanged. Other than the function f, the training data is unsupervised and consist of a set of samples from each domain. The Domain Transfer Network (DTN) we present employs a compound loss function that includes a multiclass GAN loss, an f-constancy component, and a regularizing component that encourages G to map samples from T to themselves. We apply our method to visual domains including digits and face images and demonstrate its ability to generate convincing novel images of previously unseen entities, while preserving their identity.
@cite_45: Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images. However, when it comes to the task of applying a painting's style to an anime sketch, these methods will just randomly colorize sketch lines as outputs and fail in the main task: specific style tranfer. In this paper, we integrated residual U-net to apply the style to the gray-scale sketch with auxiliary classifier generative adversarial network (AC-GAN). The whole process is automatic and fast, and the results are creditable in the quality of art style as well as colorization.
@cite_10: We developed a system for automatic generation of caricatures by analyzing unique facial features of the subject. The proposed face model contains 119 parameters derived from the MPEG-4 standard. These nodes are categorized into 8 groups with a pre-defined hierarchy to guide the placement of face components after shape exaggeration. Given an input image, the system determines which and how the face component should be altered. Using an artist’s finished work as the source image, the system is capable of producing caricatures of a similar style effectively and efficiently.
Translating photo to cartoon by computer algorithms has been studied for a long time because of the corresponding interesting and meaningful applications. The earlier work mainly relied on the analysis of facial features @cite_36 @cite_29 @cite_10 , which is hard to be applicable for large-scale faces in the wild. Thanks to the invention of GANs @cite_14 , automatic photo-to-cartoon translation becomes feasible. But most of the current related work mainly focused on the generation of anime @cite_45 or emoji @cite_6 with specific style We consider that anime, emoji, and caricature are three types of cartoon or three subsets of cartoon. . And these works have nothing to do with caricature creation that needs to be exaggerated, lifelike and artistic.