scholarly journals SliderGAN: Synthesizing Expressive Face Images by Sliding 3D Blendshape Parameters

2020 ◽  
Vol 128 (10-11) ◽  
pp. 2629-2650
Author(s):  
Evangelos Ververas ◽  
Stefanos Zafeiriou
Keyword(s):  
Input Image ◽  
Generative Adversarial Networks ◽  
Deep Convolutional Neural Networks ◽  
Regression Problem ◽  
Facial Motion ◽  
Adversarial Networks ◽  
Face Images ◽  
Image Translation ◽  
Image Pairs ◽  
Blendshape Model

Abstract Image-to-image (i2i) translation is the dense regression problem of learning how to transform an input image into an output using aligned image pairs. Remarkable progress has been made in i2i translation with the advent of deep convolutional neural networks and particular using the learning paradigm of generative adversarial networks (GANs). In the absence of paired images, i2i translation is tackled with one or multiple domain transformations (i.e., CycleGAN, StarGAN etc.). In this paper, we study the problem of image-to-image translation, under a set of continuous parameters that correspond to a model describing a physical process. In particular, we propose the SliderGAN which transforms an input face image into a new one according to the continuous values of a statistical blendshape model of facial motion. We show that it is possible to edit a facial image according to expression and speech blendshapes, using sliders that control the continuous values of the blendshape model. This provides much more flexibility in various tasks, including but not limited to face editing, expression transfer and face neutralisation, comparing to models based on discrete expressions or action units.

scholarly journals Age Progression and Regression with Spatial Attention Modules

2020 ◽  
Vol 34 (07) ◽  
pp. 11378-11385
Author(s):  
Qi Li ◽  
Yunfan Liu ◽  
Zhenan Sun
Keyword(s):  
Spatial Attention ◽  
Generative Adversarial Networks ◽  
Age Progression ◽  
Training Images ◽  
Adversarial Networks ◽  
Face Images ◽  
Multiple Datasets ◽  
Image Translation ◽  
In The Wild ◽  
Face Aging

Age progression and regression refers to aesthetically rendering a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping age-irrelevant regions unchanged.

scholarly journals Daydriex: Translating Nighttime Scenes towards Daytime Driving Experience at Night

2021 ◽  
Vol 11 (5) ◽  
pp. 2013
Author(s):  
Euihyeok Lee ◽  
Seungwoo Kang
Keyword(s):  
Input Image ◽  
Generative Adversarial Networks ◽  
User Perception ◽  
Missing Information ◽  
High Quality ◽  
Driving Experience ◽  
Processing Pipeline ◽  
Adversarial Networks ◽  
Image Translation ◽  
Translation Techniques

What if the window of our cars is a magic window, which transforms dark views outside of the window at night into bright ones as we can see in the daytime? To realize such a window, one of important requirements is that the stream of transformed images displayed on the window should be of high quality so that users perceive it as real scenes in the day. Although image-to-image translation techniques based on Generative Adversarial Networks (GANs) have been widely studied, night-to-day image translation is still a challenging task. In this paper, we propose Daydriex, a processing pipeline to generate enhanced daytime translation focusing on road views. Our key idea is to supplement the missing information in dark areas of input image frames by using existing daytime images corresponding to the input images from street view services. We present a detailed processing flow and address several issues to realize our idea. Our evaluation shows that the results by Daydriex achieves lower Fréchet Inception Distance (FID) scores and higher user perception scores compared to those by CycleGAN only.

scholarly journals Deep Fake Image Detection Based on Pairwise Learning

2020 ◽  
Vol 10 (1) ◽  
pp. 370 ◽  
Author(s):  
Chih-Chung Hsu ◽  
Yi-Xiu Zhuang ◽  
Chia-Yen Lee
Keyword(s):  
State Of The Art ◽  
Random Noise ◽  
Input Image ◽  
Generative Adversarial Networks ◽  
Source Image ◽  
Image Forgery ◽  
Social Media Networks ◽  
Adversarial Networks ◽  
Pairwise Learning ◽  
Image Pairs

Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate content can be used on social media networks, which can cause severe problems. With the aim to successfully detect fake images, an effective and efficient image forgery detector is necessary. However, conventional image forgery detectors fail to recognize fake images generated by the GAN-based generator since these images are generated and manipulated from the source image. Therefore, in this paper, we propose a deep learning-based approach for detecting the fake images by using the contrastive loss. First, several state-of-the-art GANs are employed to generate the fake–real image pairs. Next, the reduced DenseNet is developed to a two-streamed network structure to allow pairwise information as the input. Then, the proposed common fake feature network is trained using the pairwise learning to distinguish the features between the fake and real images. Finally, a classification layer is concatenated to the proposed common fake feature network to detect whether the input image is fake or real. The experimental results demonstrated that the proposed method significantly outperformed other state-of-the-art fake image detectors.

scholarly journals Unpaired Image- to- Image Translation using Cycle Generative Adversarial Networks

2020 ◽  
Vol 9 (6) ◽  
pp. 380-385
Keyword(s):  
Mapping Function ◽  
Image Synthesis ◽  
Input Image ◽  
General Purpose ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Inverse Mapping ◽  
Style Transfer ◽  
Adversarial Networks ◽  
Image Translation

In this burgeoning age and society where people are tending towards learning the benefits adversarial network we hereby benefiting the society tend to extend our research towards adversarial networks as a general-purpose solution to image-to-image translation problems. Image to image translation comes under the peripheral class of computer sciences extending our branch in the field of neural networks. We apprentice Generative adversarial networks as an optimum solution for generating Image to image translation where our motive is to learn a mapping between an input image(X) and an output image(Y) using a set of predefined pairs[4]. But it is not necessary that the paired dataset is provided to for our use and hence adversarial methods comes into existence. Further, we advance a method that is able to convert and recapture an image from a domain X to another domain Y in the absence of paired datasets. Our objective is to learn a mapping function G: A —B such that the mapping is able to distinguish the images of G(A) within the distribution of B using an adversarial loss.[1] Because this mapping is high biased, we introduce an inverse mapping function F B—A and introduce a cycle consistency loss[7]. Furthermore we wish to extend our research with various domains and involve them with neural style transfer, semantic image synthesis. Our essential commitment is to show that on a wide assortment of issues, conditional GANs produce sensible outcomes. This paper hence calls for the attention to the purpose of converting image X to image Y and we commit to the transfer learning of training dataset and optimising our code.You can find the source code for the same here.

scholarly journals Image Translation by Domain-Adversarial Training

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Zhuorong Li ◽  
Wanliang Wang ◽  
Yanwei Zhao
Keyword(s):  
Domain Adaptation ◽  
Input Image ◽  
Generative Adversarial Networks ◽  
Significant Progress ◽  
Mapping Space ◽  
Adversarial Networks ◽  
Image Prior ◽  
Image Translation ◽  
Common Framework ◽  
Adversarial Training

Image translation, where the input image is mapped to its synthetic counterpart, is attractive in terms of wide applications in fields of computer graphics and computer vision. Despite significant progress on this problem, largely due to a surge of interest in conditional generative adversarial networks (cGANs), most of the cGAN-based approaches require supervised data, which are rarely available and expensive to provide. Instead we elaborate a common framework that is also applicable to the unsupervised cases, learning the image prior by conditioning the discriminator on unaligned targets to reduce the mapping space and improve the generation quality. Besides, domain-adversarial training inspired by domain adaptation is proposed to capture discriminative and expressive features, for the purpose of improving fidelity. Effectiveness of our method is demonstrated by compelling experimental results of our method and comparisons with several baselines. As for the generality, it could be analyzed from two perspectives: adaptation to both supervised and unsupervised setting and the diversity of tasks.

scholarly journals Generative Reversible Data Hiding by Image-to-Image Translation via GANs

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Zhuo Zhang ◽  
Guangyuan Fu ◽  
Fuqiang Di ◽  
Changlong Li ◽  
Jia Liu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Input Image ◽  
Generative Adversarial Networks ◽  
Secret Message ◽  
Stego Image ◽  
Adversarial Networks ◽  
Image Translation ◽  
Realistic Image ◽  
Image Generator

The traditional reversible data hiding technique is based on cover image modification which inevitably leaves some traces of rewriting that can be more easily analyzed and attacked by the warder. Inspired by the cover synthesis steganography-based generative adversarial networks, in this paper, a novel generative reversible data hiding (GRDH) scheme by image translation is proposed. First, an image generator is used to obtain a realistic image, which is used as an input to the image-to-image translation model with CycleGAN. After image translation, a stego image with different semantic information will be obtained. The secret message and the original input image can be recovered separately by a well-trained message extractor and the inverse transform of the image translation. The experimental results have verified the effectiveness of the scheme.

scholarly journals Facial UV photo imaging for skin pigmentation assessment using conditional generative adversarial networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kaname Kojima ◽  
Kosuke Shido ◽  
Gen Tamiya ◽  
Kenshi Yamasaki ◽  
Kengo Kinoshita ◽  
...  
Keyword(s):  
Skin Pigmentation ◽  
Generative Adversarial Networks ◽  
Pigment Spot ◽  
Detection Analysis ◽  
Adversarial Networks ◽  
Skin Cancers ◽  
Uv Photography ◽  
Highly Correlated ◽  
Image Pairs ◽  
Skin Damages

AbstractSkin pigmentation is associated with skin damages and skin cancers, and ultraviolet (UV) photography is used as a minimally invasive mean for the assessment of pigmentation. Since UV photography equipment is not usually available in general practice, technologies emphasizing pigmentation in color photo images are desired for daily care. We propose a new method using conditional generative adversarial networks, named UV-photo Net, to generate synthetic UV images from color photo images. Evaluations using color and UV photo image pairs taken by a UV photography system demonstrated that pigment spots were well reproduced in synthetic UV images by UV-photo Net, and some of the reproduced pigment spots were difficult to be recognized in color photo images. In the pigment spot detection analysis, the rate of pigment spot areas in cheek regions for synthetic UV images was highly correlated with the rate for UV photo images (Pearson’s correlation coefficient 0.92). We also demonstrated that UV-photo Net was effective for floating up pigment spots for photo images taken by a smartphone camera. UV-photo Net enables an easy assessment of pigmentation from color photo images and will promote self-care of skin damages and early signs of skin cancers for preventive medicine.

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