Line drawings for face portraits from photos using global and local structure based GANs

Despite significant effort and notable success of neural style transfer, it remains challenging for highly abstract styles, in particular line drawings. In this paper, we propose APDrawingGAN++, a generative adversarial network (GAN) for transforming face photos to artistic portrait drawings (APDrawings), which addresses substantial challenges including highly abstract style, different drawing techniques for different facial features, and high perceptual sensitivity to artifacts. To address these, we propose a composite GAN architecture that consists of local networks (to learn effective representations for specific facial features) and a global network (to capture the overall content). We provide a theoretical explanation for the necessity of this composite GAN structure by proving that any GAN with a single generator cannot generate artistic styles like APDrawings. We further introduce a classification-and-synthesis approach for lips and hair where different drawing styles are used by artists, which applies suitable styles for a given input. To capture the highly abstract art form inherent in APDrawings, we address two challenging operations — (1) coping with lines with small misalignments while penalizing large discrepancy and (2) generating more continuous lines — by introducing two novel loss terms: one is a novel distance transform loss with nonlinear mapping and the other is a novel line continuity loss, both of which improve the line quality. We also develop dedicated data augmentation and pre-training to further improve results. Extensive experiments, including a user study, show that our method outperforms state-of-the-art methods, both qualitatively and quantitatively

Image Separation with Side Information: A Connected Auto-Encoders Based Approach

X-radiography (X-ray imaging) is a widely used imaging technique in art investigation. It can provide information about the condition of a painting as well as insights into an artist’s techniques and working methods, often revealing hidden information invisible to the naked eye. X-radiograpy of double-sided paintings results in a mixed X-ray image and this paper deals with the problem of separating this mixed image. Using the visible color images (RGB images) from each side of the painting, we propose a new Neural Network architecture, based upon ’connected’ auto-encoders, designed to separate the mixed X-ray image into two simulated X-ray images corresponding to each side. This connected auto-encoders architecture is such that the encoders are based on convolutional learned iterative shrinkage thresholding algorithms (CLISTA) designed using algorithm unrolling techniques, whereas the decoders consist of simple linear convolutional layers; the encoders extract sparse codes from the visible image of the front and rear paintings and mixed X-ray image, whereas the decoders reproduce both the original RGB images and the mixed X-ray image. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The methodology was tested on images from the double-sided wing panels of the Ghent Altarpiece , painted in 1432 by the brothers Hubert and Jan van Eyck. These tests show that the proposed approach outperforms other state-of-the-art X-ray image separation methods for art investigation applications

Non-photorealistic rendering of portraits

We describe an image-based non-photorealistic rendering pipeline for creating portraits in two styles: The first is a somewhat “puppet” like rendering, that treats the face like a relatively uniform smooth surface, with the geometry being emphasised by shading. The second style is inspired by the artist Julian Opie, in which the human face is reduced to its essentials, i.e. homogeneous skin, thick black lines, and facial features such as eyes and the nose represented in a cartoon manner. Our method is able to automatically generate these stylisations without requiring the input images to be tightly cropped, direct frontal view, and moreover perform abstraction while maintaining the distinctiveness of the portraits (i.e. they should remain recognisable)

Deepfakes Generated by Generative Adversarial Networks

Deep learning is a type of Artificial Intelligence (AI) that mimics the workings of the human brain in processing data such as speech recognition, visual object recognition, object detection, language translation, and making decisions. A Generative adversarial network (GAN) is a special type of deep learning, designed by Goodfellow et al. (2014), which is what we call convolution neural networks (CNN). How a GAN works is that when given a training set, they can generate new data with the same information as the training set, and this is often what we refer to as deep fakes. CNN takes an input image, assigns learnable weights and biases to various aspects of the object and is able to differentiate one from the other. This is similar to what GAN does, it creates two neural networks called discriminator and generator, and they work together to differentiate the sample input from the generated input (deep fakes). Deep fakes is a machine learning technique where a person in an existing image or video is replaced by someone else’s likeness. Deep fakes have become a problem in society because it allows anyone’s image to be co-opted and calls into question our ability to trust what we see. In this project we develop a GAN to generate deepfakes. Next, we develop a survey to determine if participants are able to identify authentic versus deep fake images. The survey employed a questionnaire asking participants their perception on AI technology based on their overall familiarity of AI, deep fake generation, reliability and trustworthiness of AI, as well as testing to see if subjects can distinguish real versus deep fake images. Results show demographic differences in perceptions of AI and that humans are good at distinguishing real images from deep fakes