Free-Form Image Inpainting Via Contrastive Attention Network

Xin Ma, Xiaoqiang Zhou, Huaibo Huang, Zhenhua Chai, Xiaolin Wei, Ran He

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Auto-TLDR; Self-supervised Siamese inference for image inpainting

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Most deep learning based image inpainting approaches adopt autoencoder or its variants to fill missing regions in images. Encoders are usually utilized to learn powerful representational spaces, which are important for dealing with sophisticated learning tasks. Specifically, in the image inpainting task, masks with any shapes can appear anywhere in images (i.e., free-form masks) forming complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. To tackle this problem, we propose a self-supervised Siamese inference network to improve the robustness and generalization. Moreover, the restored image usually can not be harmoniously integrated into the exiting content, especially in the boundary area. To address this problem, we propose a novel Dual Attention Fusion module (DAF), which can combine both the restored and known regions in a smoother way and be inserted into decoder layers in a plug-and-play way. DAF is developed to not only adaptively rescale channel-wise features by taking interdependencies between channels into account but also force deep convolutional neural networks (CNNs) focusing more on unknown regions. In this way, the unknown region will be naturally filled from the outside to the inside. Qualitative and quantitative experiments on multiple datasets, including facial and natural datasets (i.e., Celeb-HQ, Pairs Street View, Places2 and ImageNet), demonstrate that our proposed method outperforms against state-of-the-arts in generating high-quality inpainting results.

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Image Inpainting with Contrastive Relation Network

Xiaoqiang Zhou, Junjie Li, Zilei Wang, Ran He, Tieniu Tan

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Auto-TLDR; Two-Stage Inpainting with Graph-based Relation Network

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Image inpainting faces the challenging issue of the requirements on structure reasonableness and texture coherence. In this paper, we propose a two-stage inpainting framework to address this issue. The basic idea is to address the two requirements in two separate stages. Completed segmentation of the corrupted image is firstly predicted through segmentation reconstruction network, while fine-grained image details are restored in the second stage through an image generator. The two stages are connected in series as the image details are generated under the guidance of completed segmentation map that predicted in the first stage. Specifically, in the second stage, we propose a novel graph-based relation network to model the relationship existed in corrupted image. In relation network, both intra-relationship for pixels in the same semantic region and inter-relationship between different semantic parts are considered, improving the consistency and compatibility of image textures. Besides, contrastive loss is designed to facilitate the relation network training. Such a framework not only simplifies the inpainting problem directly, but also exploits the relationship in corrupted image explicitly. Extensive experiments on various public datasets quantitatively and qualitatively demonstrate the superiority of our approach compared with the state-of-the-art.

SECI-GAN: Semantic and Edge Completion for Dynamic Objects Removal

Francesco Pinto, Andrea Romanoni, Matteo Matteucci, Phil Torr

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Auto-TLDR; SECI-GAN: Semantic and Edge Conditioned Inpainting Generative Adversarial Network

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Image inpainting aims at synthesizing the missing content of damaged and corrupted images to produce visually realistic restorations; typical applications are in image restoration, automatic scene editing, super-resolution, and dynamic object removal. In this paper, we propose Semantic and Edge Conditioned Inpainting Generative Adversarial Network (SECI-GAN), an architecture that jointly exploits the high-level cues extracted by semantic segmentation and the fine-grained details captured by edge extraction to condition the image inpainting process. SECI-GAN is designed with a particular focus on recovering big regions belonging to the same object (e.g. cars or pedestrians) in the context of dynamic object removal from complex street views. To demonstrate the effectiveness of SECI-GAN, we evaluate our results on the Cityscapes dataset, showing that SECI-GAN is better than competing state-of-the-art models at recovering the structure and the content of the missing parts while producing consistent predictions.

Local Facial Attribute Transfer through Inpainting

Ricard Durall, Franz-Josef Pfreundt, Janis Keuper

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Auto-TLDR; Attribute Transfer Inpainting Generative Adversarial Network

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The term attribute transfer refers to the tasks of altering images in such a way, that the semantic interpretation of a given input image is shifted towards an intended direction, which is quantified by semantic attributes. Prominent example applications are photo realistic changes of facial features and expressions, like changing the hair color, adding a smile, enlarging the nose or altering the entire context of a scene, like transforming a summer landscape into a winter panorama. Recent advances in attribute transfer are mostly based on generative deep neural networks, using various techniques to manipulate images in the latent space of the generator. In this paper, we present a novel method for the common sub-task of local attribute transfers, where only parts of a face have to be altered in order to achieve semantic changes (e.g. removing a mustache). In contrast to previous methods, where such local changes have been implemented by generating new (global) images, we propose to formulate local attribute transfers as an inpainting problem. Removing and regenerating only parts of images, our Attribute Transfer Inpainting Generative Adversarial Network (ATI-GAN) is able to utilize local context information to focus on the attributes while keeping the background unmodified resulting in visually sound results.

Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation

Pierfrancesco Ardino, Yahui Liu, Elisa Ricci, Bruno Lepri, Marco De Nadai

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Auto-TLDR; Semantic-Guided Inpainting of Complex Urban Scene Using Semantic Segmentation and Generation

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Manipulating images of complex scenes to reconstruct, insert and/or remove specific object instances is a challenging task. Complex scenes contain multiple semantics and objects, which are frequently cluttered or ambiguous, thus hampering the performance of inpainting models. Conventional techniques often rely on structural information such as object contours in multi-stage approaches that generate unreliable results and boundaries. In this work, we propose a novel deep learning model to alter a complex urban scene by removing a user-specified portion of the image and coherently inserting a new object (e.g. car or pedestrian) in that scene. Inspired by recent works on image inpainting, our proposed method leverages the semantic segmentation to model the content and structure of the image, and learn the best shape and location of the object to insert. To generate reliable results, we design a new decoder block that combines the semantic segmentation and generation task to guide better the generation of new objects and scenes, which have to be semantically consistent with the image. Our experiments, conducted on two large-scale datasets of urban scenes (Cityscapes and Indian Driving), show that our proposed approach successfully address the problem of semantically-guided inpainting of complex urban scene.

A GAN-Based Blind Inpainting Method for Masonry Wall Images

Yahya Ibrahim, Balázs Nagy, Csaba Benedek

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Auto-TLDR; An End-to-End Blind Inpainting Algorithm for Masonry Wall Images

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In this paper we introduce a novel end-to-end blind inpainting algorithm for masonry wall images, performing the automatic detection and virtual completion of occluded or damaged wall regions. For this purpose, we propose a three-stage deep neural network that comprises a U-Net-based sub-network for wall segmentation into brick, mortar and occluded regions, which is followed by a two-stage adversarial inpainting model. The first adversarial network predicts the schematic mortar-brick pattern of the occluded areas based on the observed wall structure, providing in itself valuable structural information for archeological and architectural applications. Finally, the second adversarial network predicts the RGB pixel values yielding a realistic visual experience for the observer. While the three stages implement a sequential pipeline, they interact through dependencies of their loss functions admitting the consideration of hidden feature dependencies between the different network components. For training and testing the network a new dataset has been created, and an extensive qualitative and quantitative evaluation versus the state-of-the-art is given.

Mask-Based Style-Controlled Image Synthesis Using a Mask Style Encoder

Jaehyeong Cho, Wataru Shimoda, Keiji Yanai

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Auto-TLDR; Style-controlled Image Synthesis from Semantic Segmentation masks using GANs

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In recent years, the advances in Generative Adversarial Networks (GANs) have shown impressive results for image generation and translation tasks. In particular, the image-to-image translation is a method of learning mapping from a source domain to a target domain and synthesizing an image. Image-to-image translation can be applied to a variety of tasks, making it possible to quickly and easily synthesize realistic images from semantic segmentation masks. However, in the existing image-to-image translation method, there is a limitation on controlling the style of the translated image, and it is not easy to synthesize an image by controlling the style of each mask element in detail. Therefore, we propose an image synthesis method that controls the style of each element by improving the existing image-to-image translation method. In the proposed method, we implement a style encoder that extracts style features for each mask element. The extracted style features are concatenated to the semantic mask in the normalization layer, and used the style-controlled image synthesis of each mask element. In experiments, we train style-controlled images synthesis using the datasets consisting of semantic segmentation masks and real images. The results show that the proposed method has excellent performance for style-controlled images synthesis for each element.

Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution

Xiaoyu Xiang, Qian Lin, Jan Allebach

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Auto-TLDR; A Context-Aware Joint CAR and SR Neural Network for High-Resolution Text Recognition and Face Detection

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Due to the limits of bandwidth and storage space, digital images are usually down-scaled and compressed when transmitted over networks, resulting in loss of details and jarring artifacts that can lower the performance of high-level visual tasks. In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with an arbitrary quality factor by exploring joint compression artifacts reduction (CAR) and super-resolution (SR) tasks. First, we propose a context-aware joint CAR and SR neural network (CAJNN) that integrates both local and non-local features to solve CAR and SR in one-stage. Finally, a deep reconstruction network is adopted to predict high quality and high-resolution images. Evaluation on CAR and SR benchmark datasets shows that our CAJNN model outperforms previous methods and also takes 26.2% less runtime. Based on this model, we explore addressing two critical challenges in high-level computer vision: optical character recognition of low-resolution texts, and extremely tiny face detection. We demonstrate that CAJNN can serve as an effective image preprocessing method and improve the accuracy for real-scene text recognition (from 85.30% to 85.75%) and the average precision for tiny face detection (from 0.317 to 0.611).

Single Image Deblurring Using Bi-Attention Network

Yaowei Li, Ye Luo, Jianwei Lu

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Auto-TLDR; Bi-Attention Neural Network for Single Image Deblurring

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Recently, deep convolutional neural networks have been extensively applied into image deblurring and have achieved remarkable performance. However, most CNN-based image deblurring methods focus on simply increasing network depth, neglecting the contextual information of the blurred image and the reconstructed image. Meanwhile, most encoder-decoder based methods rarely exploit encoder's multi-layer features. To address these issues, we propose a bi-attention neural network for single image deblurring, which mainly consists of a bi-attention network and a feature fusion network. Specifically, two criss-cross attention modules are plugged before and after the encoder-decoder to capture long-range spatial contextual information in the blurred image and the reconstructed image simultaneously, and the feature fusion network combines multi-layer features from encoder to enable the decoder reconstruct the image with multi-scale features. The whole network is end-to-end trainable. Quantitative and qualitative experiment results validate that the proposed network outperforms state-of-the-art methods in terms of PSNR and SSIM on benchmark datasets.

Learning Image Inpainting from Incomplete Images using Self-Supervision

Sriram Yenamandra, Rohit Kumar Jena, Ansh Khurana, Suyash Awate

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Auto-TLDR; Unsupervised Deep Neural Network for Semantic Image Inpainting

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Current approaches for semantic image inpainting rely on deep neural networks (DNNs) that learn under full supervision, i.e., using a training set comprising pairs of (i)corrupted images with holes and (ii)corresponding uncorrupted images. However, for several real-world applications, obtaining large sets of uncorrupted images is challenging or infeasible. Current methods also rely on adversarial training involving min-max optimization that is prone to instability during learning. We propose a novel image-inpainting DNN framework that can learn in both completely unsupervised and semi-supervised modes. Moreover, our DNN learning formulation bypasses adversarial training and, thereby, lends itself to more stable training. Results on the publicly available CelebA dataset show that our method, even when learning unsupervisedly, outperforms the state of the art that learns with full supervision.

Learning Disentangled Representations for Identity Preserving Surveillance Face Camouflage

Jingzhi Li, Lutong Han, Hua Zhang, Xiaoguang Han, Jingguo Ge, Xiaochu Cao

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Auto-TLDR; Individual Face Privacy under Surveillance Scenario with Multi-task Loss Function

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In this paper, we focus on protecting the person face privacy under the surveillance scenarios, whose goal is to change the visual appearances of faces while keep them to be recognizable by current face recognition systems. This is a challenging problem as that we should retain the most important structures of captured facial images, while alter the salient facial regions to protect personal privacy. To address this problem, we introduce a novel individual face protection model, which can camouflage the face appearance from the perspective of human visual perception and preserve the identity features of faces used for face authentication. To that end, we develop an encoder-decoder network architecture that can separately disentangle the person feature representation into an appearance code and an identity code. Specifically, we first randomly divide the face image into two groups, the source set and the target set, where the source set is used to extract the identity code and the target set provides the appearance code. Then, we recombine the identity and appearance codes to synthesize a new face, which has the same identity with the source subject. Finally, the synthesized faces are used to replace the original face to protect the privacy of individual. Furthermore, our model is trained end-to-end with a multi-task loss function, which can better preserve the identity and stabilize the training loss. Experiments conducted on Cross-Age Celebrity dataset demonstrate the effectiveness of our model and validate our superiority in terms of visual quality and scalability.

Dynamic Guided Network for Monocular Depth Estimation

Xiaoxia Xing, Yinghao Cai, Yiping Yang, Dayong Wen

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Auto-TLDR; DGNet: Dynamic Guidance Upsampling for Self-attention-Decoding for Monocular Depth Estimation

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Self-attention or encoder-decoder structure has been widely used in deep neural networks for monocular depth estimation tasks. The former mechanism are capable to capture long-range information by computing the representation of each position by a weighted sum of the features at all positions, while the latter networks can capture structural details information by gradually recovering the spatial information. In this work, we combine the advantages of both methods. Specifically, our proposed model, DGNet, extends EMANet Network by adding an effective decoder module to refine the depth results. In the decoder stage, we further design dynamic guidance upsampling which uses local neighboring information of low-level features guide coarser depth to upsample. In this way, dynamic guidance upsampling generates content-dependent and spatially-variant kernels for depth upsampling which makes full use of spatial details information from low-level features. Experimental results demonstrate that our method obtains higher accuracy and generates the desired depth map.

Unsupervised Contrastive Photo-To-Caricature Translation Based on Auto-Distortion

Yuhe Ding, Xin Ma, Mandi Luo, Aihua Zheng, Ran He

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Auto-TLDR; Unsupervised contrastive photo-to-caricature translation with style loss

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Photo-to-caricature aims to synthesize the caricature as a rendered image exaggerating the features through sketching, pencil strokes, or other artistic drawings. Style rendering and geometry deformation are the most important aspects in photo-to-caricature translation task. To take both into consideration, we propose an unsupervised contrastive photo-to-caricature translation architecture. Considering the intuitive artifacts in the existing methods, we propose a contrastive style loss for style rendering to enforce the similarity between the style of rendered photo and the caricature, and simultaneously enhance its discrepancy to the photos. To obtain an exaggerating deformation in an unpaired/unsupervised fashion, we propose a Distortion Prediction Module (DPM) to predict a set of displacements vectors for each input image while fixing some controlling points, followed by the thin plate spline interpolation for warping. The model is trained on unpaired photo and caricature while can offer bidirectional synthesizing via inputting either a photo or a caricature. Extensive experiments demonstrate that the proposed model is effective to generate hand-drawn like caricatures compared with existing competitors.

SIDGAN: Single Image Dehazing without Paired Supervision

Pan Wei, Xin Wang, Lei Wang, Ji Xiang, Zihan Wang

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Auto-TLDR; DehazeGAN: An End-to-End Generative Adversarial Network for Image Dehazing

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Single image dehazing is challenging without scene airlight and transmission map. Most of existing dehazing algorithms tend to estimate key parameters based on manual designed priors or statistics, which may be invalid in some scenarios. Although deep learning-based dehazing methods provide an effective solution, most of them rely on paired training datasets, which are prohibitively difficult to be collected in real world. In this paper, we propose an effective end-to-end generative adversarial network for image dehazing, named DehazeGAN. The proposed DehazeGAN adopts a U-net architecture with a novel color-consistency loss derived from dark channel prior and perceptual loss, which can be trained in an unsupervised fashion without paired synthetic datasets. We create a RealHaze dataset for network training, including 4,000 outdoor hazy images and 4,000 haze-free images. Extensive experiments demonstrate that our proposed DehazeGAN achieves better performance than existing state-of-the-art methods on both synthetic datasets and real-world datasets in terms of PSNR, SSIM, and subjective visual experience.

GarmentGAN: Photo-Realistic Adversarial Fashion Transfer

Amir Hossein Raffiee, Michael Sollami

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Auto-TLDR; GarmentGAN: A Generative Adversarial Network for Image-Based Garment Transfer

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The garment transfer problem comprises two tasks: learning to separate a person's body (pose, shape, color) from their clothing (garment type, shape, style) and then generating new images of the wearer dressed in arbitrary garments. We present GarmentGAN, a new algorithm that performs image-based garment transfer through generative adversarial methods. The GarmentGAN framework allows users to virtually try-on items before purchase and generalizes to various apparel types. GarmentGAN requires as input only two images, namely, a picture of the target fashion item and an image containing the customer. The output is a synthetic image wherein the customer is wearing the target apparel. In order to make the generated image look photo-realistic, we employ the use of novel generative adversarial techniques. GarmentGAN improves on existing methods in the realism of generated imagery and solves various problems related to self-occlusions. Our proposed model incorporates additional information during training, utilizing both segmentation maps and body key-point information. We show qualitative and quantitative comparisons to several other networks to demonstrate the effectiveness of this technique.

Let's Play Music: Audio-Driven Performance Video Generation

Hao Zhu, Yi Li, Feixia Zhu, Aihua Zheng, Ran He

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Auto-TLDR; APVG: Audio-driven Performance Video Generation Using Structured Temporal UNet

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We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multi-staged framework to achieve this new task to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. Then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap offers more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and inter-frame temporal consistency. They are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework.

Global-Local Attention Network for Semantic Segmentation in Aerial Images

Minglong Li, Lianlei Shan, Weiqiang Wang

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Auto-TLDR; GLANet: Global-Local Attention Network for Semantic Segmentation

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Errors in semantic segmentation task could be classified into two types: large area misclassification and local inaccurate boundaries. Previously attention based methods capture rich global contextual information, this is beneficial to diminish the first type of error, but local imprecision still exists. In this paper we propose Global-Local Attention Network (GLANet) with a simultaneous consideration of global context and local details. Specifically, our GLANet is composed of two branches namely global attention branch and local attention branch, and three different modules are embedded in the two branches for the purpose of modeling semantic interdependencies in spatial, channel and boundary dimensions respectively. We sum the outputs of the two branches to further improve feature representation, leading to more precise segmentation results. The proposed method achieves very competitive segmentation accuracy on two public aerial image datasets, bringing significant improvements over baseline.

Residual Fractal Network for Single Image Super Resolution by Widening and Deepening

Jiahang Gu, Zhaowei Qu, Xiaoru Wang, Jiawang Dan, Junwei Sun

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Auto-TLDR; Residual fractal convolutional network for single image super-resolution

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The architecture of the convolutional neural network (CNN) plays an important role in single image super-resolution (SISR). However, most models proposed in recent years usually transplant methods or architectures that perform well in other vision fields. Thence they do not combine the characteristics of super-resolution (SR) and ignore the key information brought by the recurring texture feature in the image. To utilize patch-recurrence in SR and the high correlation of texture, we propose a residual fractal convolutional block (RFCB) and expand its depth and width to obtain residual fractal network (RFN), which contains deep residual fractal network (DRFN) and wide residual fractal network (WRFN). RFCB is recursive with multiple branches of magnified receptive field. Through the phased feature fusion module, the network focuses on extracting high-frequency texture feature that repeatedly appear in the image. We also introduce residual in residual (RIR) structure to RFCB that enables abundant low-frequency feature feed into deeper layers and reduce the difficulties of network training. RFN is the first supervised learning method to combine the patch-recurrence characteristic in SISR into network design. Extensive experiments demonstrate that RFN outperforms state-of-the-art SISR methods in terms of both quantitative metrics and visual quality, while the amount of parameters has been greatly optimized.

UCCTGAN: Unsupervised Clothing Color Transformation Generative Adversarial Network

Shuming Sun, Xiaoqiang Li, Jide Li

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Auto-TLDR; An Unsupervised Clothing Color Transformation Generative Adversarial Network

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Clothing color transformation refers to changing the clothes color in an original image to the clothes color in a target image. In this paper, we propose an Unsupervised Clothing Color Transformation Generative Adversarial Network (UCCTGAN) for the task. UCCTGAN adopts the color histogram of a target clothes as color guidance and an improved U-net architecture called AntennaNet is put forward to fuse the extracted color information with the original image. Meanwhile, to accomplish unsupervised learning, the loss function is carefully designed according to color moment, which evaluates the chromatic aberration between the target clothing and the generated clothing. Experimental results show that our network has the ability to generate convincing color transformation results.

Face Super-Resolution Network with Incremental Enhancement of Facial Parsing Information

Shuang Liu, Chengyi Xiong, Zhirong Gao

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Auto-TLDR; Learning-based Face Super-Resolution with Incremental Boosting Facial Parsing Information

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Recently, facial priors based face super-resolution (SR) methods have obtained significant performance gains in dealing with extremely degraded facial images, and facial priors have also been proved useful in facilitating the inference of face images. Based on this, how to fully fuse facial priors into deep features to improve face SR performance has attracted a major attention. In this paper, we propose a learning-based face SR approach with incremental boosting facial parsing information (IFPSR) for high-magnification of low-resolution faces. The proposed IFPSR method consists of three main parts: i) a three-stage parsing map embedded features upsampling network, in which image recovery and prior estimation processes are performed simultaneously and progressively to improve the image resolution; ii) a progressive training method and a joint facial attention and heatmap loss to obtain better facial attributes; iii) the channel attention strategy in residual dense blocks to adaptively learn facial features. Extensive experimental results show that compared with the state-of-the-art methods in terms of quantitative and qualitative metrics, our approach can achieve an outstanding balance between SR image quality and low network complexity.

Attributes Aware Face Generation with Generative Adversarial Networks

Zheng Yuan, Jie Zhang, Shiguang Shan, Xilin Chen

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Auto-TLDR; AFGAN: A Generative Adversarial Network for Attributes Aware Face Image Generation

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Recent studies have shown remarkable success in face image generations. However, most of the existing methods only generate face images from random noise, and cannot generate face images according to the specific attributes. In this paper, we focus on the problem of face synthesis from attributes, which aims at generating faces with specific characteristics corresponding to the given attributes. To this end, we propose a novel attributes aware face image generator method with generative adversarial networks called AFGAN. Specifically, we firstly propose a two-path embedding layer and self-attention mechanism to convert binary attribute vector to rich attribute features. Then three stacked generators generate 64 * 64, 128 * 128 and 256 * 256 resolution face images respectively by taking the attribute features as input. In addition, an image-attribute matching loss is proposed to enhance the correlation between the generated images and input attributes. Extensive experiments on CelebA demonstrate the superiority of our AFGAN in terms of both qualitative and quantitative evaluations.

Arbitrary Style Transfer with Parallel Self-Attention

Tiange Zhang, Ying Gao, Feng Gao, Lin Qi, Junyu Dong

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Auto-TLDR; Self-Attention-Based Arbitrary Style Transfer Using Adaptive Instance Normalization

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Neural style transfer aims to create artistic images by synthesizing patterns from a given style image. Recently, the Adaptive Instance Normalization (AdaIN) layer is proposed to achieve real-time arbitrary style transfer. However, we observed that if crucial features based on AdaIN can be further emphasized during transfer, both content and style information will be better reflected in stylized images. Furthermore, it is always essential to preserve more details and reduce unexpected artifacts in order to generate appealing results. In this paper, we introduce an improved arbitrary style transfer method based on the self-attention mechanism. A self-attention module is designed to learn what and where to emphasize in the input image. In addition, an extra Laplacian loss is applied to preserve structure details of the content while eliminating artifacts. Experimental results demonstrate that the proposed method outperforms AdaIN and can generate more appealing results.

Few-Shot Font Generation with Deep Metric Learning

Haruka Aoki, Koki Tsubota, Hikaru Ikuta, Kiyoharu Aizawa

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Auto-TLDR; Deep Metric Learning for Japanese Typographic Font Synthesis

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Designing fonts for languages with a large number of characters, such as Japanese and Chinese, is an extremely labor-intensive and time-consuming task. In this study, we addressed the problem of automatically generating Japanese typographic fonts from only a few font samples, where the synthesized glyphs are expected to have coherent characteristics, such as skeletons, contours, and serifs. Existing methods often fail to generate fine glyph images when the number of style reference glyphs is extremely limited. Herein, we proposed a simple but powerful framework for extracting better style features. This framework introduces deep metric learning to style encoders. We performed experiments using black-and-white and shape-distinctive font datasets and demonstrated the effectiveness of the proposed framework.

Multi-Laplacian GAN with Edge Enhancement for Face Super Resolution

Shanlei Ko, Bi-Ru Dai

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Auto-TLDR; Face Image Super-Resolution with Enhanced Edge Information

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Face image super-resolution has become a research hotspot in the field of image processing. Nowadays, more and more researches add additional information, such as landmark, identity, to reconstruct high resolution images from low resolution ones, and have a good performance in quantitative terms and perceptual quality. However, these additional information is hard to obtain in many cases. In this work, we focus on reconstructing face images by extracting useful information from face images directly rather than using additional information. By observing edge information in each scale of face images, we propose a method to reconstruct high resolution face images with enhanced edge information. In additional, with the proposed training procedure, our method reconstructs photo-realistic images in upscaling factor 8x and outperforms state-of-the-art methods both in quantitative terms and perceptual quality.

Future Urban Scenes Generation through Vehicles Synthesis

Alessandro Simoni, Luca Bergamini, Andrea Palazzi, Simone Calderara, Rita Cucchiara

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Auto-TLDR; Predicting the Future of an Urban Scene with a Novel View Synthesis Paradigm

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In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene. Despite recent advances, generating the entire scene in an end-to-end fashion is still far from being achieved. Instead, here we follow a two stages approach, where interpretable information is included in the loop and each actor is modelled independently. We leverage a per-object novel view synthesis paradigm; i.e. generating a synthetic representation of an object undergoing a geometrical roto-translation in the 3D space. Our model can be easily conditioned with constraints (e.g. input trajectories) provided by state-of-the-art tracking methods or by the user itself. This allows us to generate a set of diverse realistic futures starting from the same input in a multi-modal fashion. We visually and quantitatively show the superiority of this approach over traditional end-to-end scene-generation methods on CityFlow, a challenging real world dataset.

Selective Kernel and Motion-Emphasized Loss Based Attention-Guided Network for HDR Imaging of Dynamic Scenes

Yipeng Deng, Qin Liu, Takeshi Ikenaga

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Auto-TLDR; SK-AHDRNet: A Deep Network with attention module and motion-emphasized loss function to produce ghost-free HDR images

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Ghost-like artifacts caused by ill-exposed and motion areas is one of the most challenging problems in high dynamic range (HDR) image reconstruction.When the motion range is small, previous methods based on optical flow or patch-match can suppress ghost-like artifacts by first aligning input images before merging them.However, they are not robust enough and still produce artifacts for challenging scenes where large foreground motions exist.To this end, we propose a deep network with attention module and motion-emphasized loss function to produce ghost-free HDR images. In attention module, we use channel and spatial attention to guide network to emphasize important components such as motion and saturated areas automatically. With the purpose of being robust to images with different resolutions and objects with distinct scale, we adopt the selective kernel network as the basic framework for channel attention. In addition to the attention module, the motion-emphasized loss function based on the motion and ill-exposed areas mask is designed to help network reconstruct motion areas. Experiments on the public dataset indicate that the proposed SK-AHDRNet produces ghost-free results where detail in ill-exposed areas is well recovered. The proposed method scores 43.17 with PSNR metric and 61.02 with HDR-VDP-2 metric on test which outperforms all conventional works. According to quantitative and qualitative evaluations, the proposed method can achieve state-of-the-art performance.

Dual-MTGAN: Stochastic and Deterministic Motion Transfer for Image-To-Video Synthesis

Fu-En Yang, Jing-Cheng Chang, Yuan-Hao Lee, Yu-Chiang Frank Wang

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Auto-TLDR; Dual Motion Transfer GAN for Convolutional Neural Networks

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Generating videos with content and motion variations is a challenging task in computer vision. While the recent development of GAN allows video generation from latent representations, it is not easy to produce videos with particular content of motion patterns of interest. In this paper, we propose Dual Motion Transfer GAN (Dual-MTGAN), which takes image and video data as inputs while learning disentangled content and motion representations. Our Dual-MTGAN is able to perform deterministic motion transfer and stochastic motion generation. Based on a given image, the former preserves the input content and transfers motion patterns observed from another video sequence, and the latter directly produces videos with plausible yet diverse motion patterns based on the input image. The proposed model is trained in an end-to-end manner, without the need to utilize pre-defined motion features like pose or facial landmarks. Our quantitative and qualitative results would confirm the effectiveness and robustness of our model in addressing such conditioned image-to-video tasks.

Adaptive Image Compression Using GAN Based Semantic-Perceptual Residual Compensation

Ruojing Wang, Zitang Sun, Sei-Ichiro Kamata, Weili Chen

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Auto-TLDR; Adaptive Image Compression using GAN based Semantic-Perceptual Residual Compensation

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Image compression is a basic task in image processing. In this paper, We present an adaptive image compression algorithm that relies on GAN based semantic-perceptual residual compensation, which is available to offer visually pleasing reconstruction at a low bitrate. Our method adopt an U-shaped encoding and decoding structure accompanied by a well-designed dense residual connection with strip pooling module to improve the original auto-encoder. Besides, we introduce the idea of adversarial learning by introducing a discriminator thus constructed a complete GAN. To improve the coding efficiency, we creatively designed an adaptive semantic-perception residual compensation block based on Grad-CAM algorithm. In the improvement of the quantizer, we embed the method of soft-quantization so as to solve the problem to some extent that back propagation process is irreversible. Simultaneously, we use the latest FLIF lossless compression algorithm and BPG vector compression algorithm to perform deeper compression on the image. More importantly experimental results including PSNR, MS-SSIM demonstrate that the proposed approach outperforms the current state-of-the-art image compression methods.

Novel View Synthesis from a 6-DoF Pose by Two-Stage Networks

Xiang Guo, Bo Li, Yuchao Dai, Tongxin Zhang, Hui Deng

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Auto-TLDR; Novel View Synthesis from a 6-DoF Pose Using Generative Adversarial Network

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Novel view synthesis is a challenging problem in 3D vision and robotics. Different from the existing works, which need the reference images or 3D model, we propose a novel paradigm to this problem. That is, we synthesize the novel view from a 6-DoF pose directly. Although this setting is the most straightforward way, there are few works addressing it. While, our experiments demonstrate that, with a concise CNN, we could get a meaningful parametric model which could reconstruct the correct scenery images only from the 6-DoF pose. To this end, we propose a two-stage learning strategy, which consists of two consecutive CNNs: GenNet and RefineNet. The GenNet generates a coarse image from a camera pose. The RefineNet is a generative adversarial network that could refine the coarse image. In this way, we decouple the geometric relationship mapping and texture detail rendering. Extensive experiments conducted on the public datasets prove the effectiveness of our method. We believe this paradigm is of high research and application value and could be an important direction in novel view synthesis. We will share our code after the acceptance of this work.

High Resolution Face Age Editing

Xu Yao, Gilles Puy, Alasdair Newson, Yann Gousseau, Pierre Hellier

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Auto-TLDR; An Encoder-Decoder Architecture for Face Age editing on High Resolution Images

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Face age editing has become a crucial task in film post-production, and is also becoming popular for general purpose photography. Recently, adversarial training has produced some of the most visually impressive results for image manipulation, including the face aging/de-aging task. In spite of considerable progress, current methods often present visual artifacts and can only deal with low-resolution images. In order to achieve aging/de-aging with the high quality and robustness necessary for wider use, these problems need to be addressed. This is the goal of the present work. We present an encoder-decoder architecture for face age editing. The core idea of our network is to encode a face image to age-invariant features, and learn a modulation vector corresponding to a target age. We then combine these two elements to produce a realistic image of the person with the desired target age. Our architecture is greatly simplified with respect to other approaches, and allows for fine-grained age editing on high resolution images in a single unified model. Source codes are available at https://github.com/InterDigitalInc/HRFAE.

Interactive Style Space of Deep Features and Style Innovation

Bingqing Guo, Pengwei Hao

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Auto-TLDR; Interactive Style Space of Convolutional Neural Network Features

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Stylizing images as paintings has been a popular computer vision technique for a long time. However, most studies only consider the art styles known today, and rarely have investigated styles that have not been painted yet. We fill this gap by projecting the high-dimensional style space of Convolutional Neural Network features to the latent low-dimensional style manifold space. It is worth noting that in our visualized space, simple style linear interpolation is enabled to generate new artistic styles that would revolutionize the future of art in technology. We propose a model of an Interactive Style Space (ISS) to prove that in a manifold style space, the unknown styles are obtainable through interpolation of known styles. We verify the correctness and feasibility of our Interactive Style Space (ISS) and validate style interpolation within the space.

Multi-Scale Residual Pyramid Attention Network for Monocular Depth Estimation

Jing Liu, Xiaona Zhang, Zhaoxin Li, Tianlu Mao

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Auto-TLDR; Multi-scale Residual Pyramid Attention Network for Monocular Depth Estimation

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Monocular depth estimation is a challenging problem in computer vision and is crucial for understanding 3D scene geometry. Recently, deep convolutional neural networks (DCNNs) based methods have improved the estimation accuracy significantly. However, existing methods fail to consider complex textures and geometries in scenes, thereby resulting in loss of local details, distorted object boundaries, and blurry reconstruction. In this paper, we proposed an end-to-end Multi-scale Residual Pyramid Attention Network (MRPAN) to mitigate these problems.First,we propose a Multi-scale Attention Context Aggregation (MACA) module, which consists of Spatial Attention Module (SAM) and Global Attention Module (GAM). By considering the position and scale correlation of pixels from spatial and global perspectives, the proposed module can adaptively learn the similarity between pixels so as to obtain more global context information of the image and recover the complex structure in the scene. Then we proposed an improved Residual Refinement Module (RRM) to further refine the scene structure, giving rise to deeper semantic information and retain more local details. Experimental results show that our method achieves more promisin performance in object boundaries and local details compared with other state-of-the-art methods.

Progressive Splitting and Upscaling Structure for Super-Resolution

Qiang Li, Tao Dai, Shutao Xia

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Auto-TLDR; PSUS: Progressive and Upscaling Layer for Single Image Super-Resolution

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Recently, very deep convolutional neural networks (CNNs) have shown great success in single image super-resolution (SISR). Most of these methods focus on the design of network architecture and adopt a sub-pixel convolution layer at the end of network, but few have paid attention to exploring potential representation ability of upscaling layer. Sub-pixel convolution layer aggregates several low resolution (LR) feature maps and builds super-resolution (SR) images in a single step. However, those LR feature maps share similar patterns as they are extracted from a single trunk network. We believe that the mapping relationships between input image and each LR feature map are not consistent. Inspired by this, we propose a novel progressive splitting and upscaling structure, termed PSUS, which generates decoupled feature maps for upscaling layer to get better SR image. Experiments show that our method can not only speed up the convergence, but also achieve considerable improvement on image quality with fewer parameters and lower computational complexity.

Dual-Attention Guided Dropblock Module for Weakly Supervised Object Localization

Junhui Yin, Siqing Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo

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Auto-TLDR; Dual-Attention Guided Dropblock for Weakly Supervised Object Localization

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Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL) and propose the dual-attention guided dropblock module (DGDM), which aims at learning the informative and complementary visual patterns for WSOL. This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD). To model channel interdependencies, the CAGD ranks the channel attentions and treats the top-k attentions with the largest magnitudes as the important ones. It also keeps some low-valued elements to increase their value if they become important during training. The SAGD can efficiently remove the most discriminative information by erasing the contiguous regions of feature maps rather than individual pixels. This guides the model to capture the less discriminative parts for classification. Furthermore, it can also distinguish the foreground objects from the background regions to alleviate the attention misdirection. Experimental results demonstrate that the proposed method achieves new state-of-the-art localization performance.

Unsupervised Face Manipulation Via Hallucination

Keerthy Kusumam, Enrique Sanchez, Georgios Tzimiropoulos

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Auto-TLDR; Unpaired Face Image Manipulation using Autoencoders

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This paper addresses the problem of manipulatinga face image in terms of changing its pose. To achieve this, wepropose a new method that can be trained under the very general“unpaired” setting. To this end, we firstly propose to modelthe general appearance, layout and background of the inputimage using a low-resolution version of it which is progressivelypassed through a hallucination network to generate featuresat higher resolutions. We show that such a formulation issignificantly simpler than previous approaches for appearancemodelling based on autoencoders. Secondly, we propose a fullylearnable and spatially-aware appearance transfer module whichcan cope with misalignment between the input source image andthe target pose and can effectively combine the features fromthe hallucination network with the features produced by ourgenerator. Thirdly, we introduce an identity preserving methodthat is trained in an unsupervised way, by using an auxiliaryfeature extractor and a contrastive loss between the real andgenerated images. We compare our method against the state-of-the-art reporting significant improvements both quantitatively, interms of FID and IS, and qualitatively.

Cascade Attention Guided Residue Learning GAN for Cross-Modal Translation

Bin Duan, Wei Wang, Hao Tang, Hugo Latapie, Yan Yan

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Auto-TLDR; Cascade Attention-Guided Residue GAN for Cross-modal Audio-Visual Learning

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Since we were babies, we intuitively develop the ability to correlate the input from different cognitive sensors such as vision, audio, and text. However, in machine learning, this cross-modal learning is a nontrivial task because different modalities have no homogeneous properties. Previous works discover that there should be bridges among different modalities. From neurology and psychology perspective, humans have the capacity to link one modality with another one, e.g., associating a picture of a bird with the only hearing of its singing and vice versa. Is it possible for machine learning algorithms to recover the scene given the audio signal? In this paper, we propose a novel Cascade Attention-Guided Residue GAN (CAR-GAN), aiming at reconstructing the scenes given the corresponding audio signals. Particularly, we present a residue module to mitigate the gap between different modalities progressively. Moreover, a cascade attention guided network with a novel classification loss function is designed to tackle the cross-modal learning task. Our model keeps consistency in the high-level semantic label domain and is able to balance two different modalities. The experimental results demonstrate that our model achieves the state-of-the-art cross-modal audio-visual generation on the challenging Sub-URMP dataset.

Cycle-Consistent Adversarial Networks and Fast Adaptive Bi-Dimensional Empirical Mode Decomposition for Style Transfer

Elissavet Batziou, Petros Alvanitopoulos, Konstantinos Ioannidis, Ioannis Patras, Stefanos Vrochidis, Ioannis Kompatsiaris

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Auto-TLDR; FABEMD: Fast and Adaptive Bidimensional Empirical Mode Decomposition for Style Transfer on Images

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Recently, research endeavors have shown the potentiality of Cycle-Consistent Adversarial Networks (CycleGAN) in style transfer. In Cycle-Consistent Adversarial Networks, the consistency loss is introduced to measure the difference between the original images and the reconstructed in both directions, forward and backward. In this work, the combination of Cycle-Consistent Adversarial Networks with Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) is proposed to perform style transfer on images. In the proposed approach the cycle-consistency loss is modified to include the differences between the extracted Intrinsic Mode Functions (BIMFs) images. Instead of an estimation of pixel-to-pixel difference between the produced and input images, the FABEMD is applied and the extracted BIMFs are involved in the computation of the total cycle loss. This method enriches the computation of the total loss in a content-to-content and style-to-style comparison by connecting the spatial information to the frequency components. The experimental results reveal that the proposed method is efficient and produces qualitative results comparable to state-of-the-art methods.

Cross-Layer Information Refining Network for Single Image Super-Resolution

Hongyi Zhang, Wen Lu, Xiaopeng Sun

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Auto-TLDR; Interlaced Spatial Attention Block for Single Image Super-Resolution

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Recently, deep learning-based image super-resolution (SR) has made a remarkable progress. However, previous SR methods rarely focus on the correlation between adjacent layers, which leads to underutilization of the information extracted by each convolutional layer. To address these problem, we design a simple and efficient cross-layer information refining network (CIRN) for single image super-resolution. Concretely, we propose the interlaced spatial attention block (ISAB) to measure the correlation between the adjacent layers feature maps and adaptively rescale spatial-wise features for refining the information. Owing to the two stage information propagation strategy, the CIRN can distill the primary information of adjacent layers without introducing too many parameters. Extensive experiments on benchmark datasets illustrate that our method achieves better accuracy than state-of-the-art methods even in 16× scale, spcifically it has a better banlance between performance and parameters.

Progressive Scene Segmentation Based on Self-Attention Mechanism

Yunyi Pan, Yuan Gan, Kun Liu, Yan Zhang

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Auto-TLDR; Two-Stage Semantic Scene Segmentation with Self-Attention

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Semantic scene segmentation is vital for a large variety of applications as it enables understanding of 3D data. Nowadays, various approaches based upon point clouds ignore the mathematical distribution of points and treat the points equally. The methods following this direction neglect the imbalance problem of samples that naturally exists in scenes. To avoid these issues, we propose a two-stage semantic scene segmentation framework based on self-attention mechanism and achieved state-of-the-art performance on 3D scene understanding tasks. We split the whole task into two small ones which efficiently relief the sample imbalance issue. In addition, we have designed a new self-attention block which could be inserted into submanifold convolution networks to model the long-range dependencies that exists among points. The proposed network consists of an encoder and a decoder, with the spatial-wise and channel-wise attention modules inserted. The two-stage network shares a U-Net architecture and is an end-to-end trainable framework which could predict the semantic label for the scene point clouds fed into it. Experiments on standard benchmarks of 3D scenes implies that our network could perform at par or better than the existing state-of-the-art methods.

Deep Universal Blind Image Denoising

Jae Woong Soh, Nam Ik Cho

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Auto-TLDR; Image Denoising with Deep Convolutional Neural Networks

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Image denoising is an essential part of many image processing and computer vision tasks due to inevitable noise corruption during image acquisition. Traditionally, many researchers have investigated image priors for the denoising, within the Bayesian perspective based on image properties and statistics. Recently, deep convolutional neural networks (CNNs) have shown great success in image denoising by incorporating large-scale synthetic datasets. However, they both have pros and cons. While the deep CNNs are powerful for removing the noise with known statistics, they tend to lack flexibility and practicality for the blind and real-world noise. Moreover, they cannot easily employ explicit priors. On the other hand, traditional non-learning methods can involve explicit image priors, but they require considerable computation time and cannot exploit large-scale external datasets. In this paper, we present a CNN-based method that leverages the advantages of both methods based on the Bayesian perspective. Concretely, we divide the blind image denoising problem into sub-problems and conquer each inference problem separately. As the CNN is a powerful tool for inference, our method is rooted in CNNs and propose a novel design of network for efficient inference. With our proposed method, we can successfully remove blind and real-world noise, with a moderate number of parameters of universal CNN.

VITON-GT: An Image-Based Virtual Try-On Model with Geometric Transformations

Matteo Fincato, Federico Landi, Marcella Cornia, Fabio Cesari, Rita Cucchiara

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Auto-TLDR; VITON-GT: An Image-based Virtual Try-on Architecture for Fashion Catalogs

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The large spread of online shopping has led computer vision researchers to develop different solutions for the fashion domain to potentially increase the online user experience and improve the efficiency of preparing fashion catalogs. Among them, image-based virtual try-on has recently attracted a lot of attention resulting in several architectures that can generate a new image of a person wearing an input try-on garment in a plausible and realistic way. In this paper, we present VITON-GT, a new model for virtual try-on that generates high-quality and photo-realistic images thanks to multiple geometric transformations. In particular, our model is composed of a two-stage geometric transformation module that performs two different projections on the input garment, and a transformation-guided try-on module that synthesize the new image. We experimentally validate the proposed solution on the most common dataset for this task, containing mainly t-shirts, and we demonstrate its effectiveness compared to different baselines and previous methods. Additionally, we assess the generalization capabilities of our model on a new set of fashion items composed of upper-body clothes from different categories. To the best of our knowledge, we are the first to test virtual try-on architectures in this challenging experimental setting.

PSDNet: A Balanced Architecture of Accuracy and Parameters for Semantic Segmentation

Yue Liu, Zhichao Lian

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Auto-TLDR; Pyramid Pooling Module with SE1Cblock and D2SUpsample Network (PSDNet)

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Abstract—In this paper, we present our Pyramid Pooling Module （PPM） with SE1Cblock and D2SUpsample Network (PSDNet), a novel architecture for accurate semantic segmentation. Started from the known work called Pyramid Scene Parsing Network (PSPNet), PSDNet takes advantage of pyramid pooling structure with channel attention module and feature transform module in Pyramid Pooling Module (PPM). The enhanced PPM with these two components can strengthen context information flowing in the network instead of damaging it. The channel attention module we mentioned is an improved “Squeeze and Excitation with 1D Convolution” (SE1C) block which can explicitly model interrelationship between channels with fewer number of parameters. We propose a feature transform module named “Depth to Space Upsampling” (D2SUpsample) in the PPM which keeps integrity of features by transforming features while interpolating features, at the same time reducing parameters. In addition, we introduce a joint strategy in SE1Cblock which combines two variants of global pooling without increasing parameters. Compared with PSPNet, our work achieves higher accuracy on public datasets with 73.97% mIoU and 82.89% mAcc accuracy on Cityscapes Dataset based on ResNet50 backbone.

Continuous Learning of Face Attribute Synthesis

Ning Xin, Shaohui Xu, Fangzhe Nan, Xiaoli Dong, Weijun Li, Yuanzhou Yao

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Auto-TLDR; Continuous Learning for Face Attribute Synthesis

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The generative adversarial network (GAN) exhibits great superiority in the face attribute synthesis task. However, existing methods have very limited effects on the expansion of new attributes. To overcome the limitations of a single network in new attribute synthesis, a continuous learning method for face attribute synthesis is proposed in this work. First, the feature vector of the input image is extracted and attribute direction regression is performed in the feature space to obtain the axes of different attributes. The feature vector is then linearly guided along the axis so that images with target attributes can be synthesized by the decoder. Finally, to make the network capable of continuous learning, the orthogonal direction modification module is used to extend the newly-added attributes. Experimental results show that the proposed method can endow a single network with the ability to learn attributes continuously, and, as compared to those produced by the current state-of-the-art methods, the synthetic attributes have higher accuracy.

RSAN: Residual Subtraction and Attention Network for Single Image Super-Resolution

Shuo Wei, Xin Sun, Haoran Zhao, Junyu Dong

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Auto-TLDR; RSAN: Residual subtraction and attention network for super-resolution

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The single-image super-resolution (SISR) aims to recover a potential high-resolution image from its low-resolution version. Recently, deep learning-based methods have played a significant role in super-resolution field due to its effectiveness and efficiency. However, most of the SISR methods neglect the importance among the feature map channels. Moreover, they can not eliminate the redundant noises, making the output image be blurred. In this paper, we propose the residual subtraction and attention network (RSAN) for powerful feature expression and channels importance learning. More specifically, RSAN firstly implements one redundance removal module to learn noise information in the feature map and subtract noise through residual learning. Then it introduces the channel attention module to amplify high-frequency information and suppress the weight of effectless channels. Experimental results on extensive public benchmarks demonstrate our RSAN achieves significant improvement over the previous SISR methods in terms of both quantitative metrics and visual quality.

Detail-Revealing Deep Low-Dose CT Reconstruction

Xinchen Ye, Yuyao Xu, Rui Xu, Shoji Kido, Noriyuki Tomiyama

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Auto-TLDR; A Dual-branch Aggregation Network for Low-Dose CT Reconstruction

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Low-dose CT imaging emerges with low radiation risk due to the reduction of radiation dose, but brings negative impact on the imaging quality. This paper addresses the problem of low-dose CT reconstruction. Previous methods are unsatisfactory due to the inaccurate recovery of image details under the strong noise generated by the reduction of radiation dose, which directly affects the final diagnosis. To suppress the noise effectively while retain the structures well, we propose a detail-revealing dual-branch aggregation network to effectively reconstruct the degraded CT image. Specifically, the main reconstruction branch iteratively exploits and compensates the reconstruction errors to gradually refine the CT image, while the prior branch is to learn the structure details as prior knowledge to help recover the CT image. A sophisticated detail-revealing loss is designed to fuse the information from both branches and guide the learning to obtain better performance from pixel-wise and holistic perspectives respectively. Experimental results show that our method outperforms the state-of-art methods in both PSNR and SSIM metrics.

An Unsupervised Approach towards Varying Human Skin Tone Using Generative Adversarial Networks

Debapriya Roy, Diganta Mukherjee, Bhabatosh Chanda

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Auto-TLDR; Unsupervised Skin Tone Change Using Augmented Reality Based Models

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With the increasing popularity of augmented and virtual reality, retailers are now more focusing towards customer satisfaction to increase the amount of sales. Although augmented reality is not a new concept but it has gained its much needed attention over the past few years. Our present work is targeted towards this direction which may be used to enhance user experience in various virtual and augmented reality based applications. We propose a model to change skin tone of person. Given any input image of a person or a group of persons with some value indicating the desired change of skin color towards fairness or darkness, this method can change the skin tone of the persons in the image. This is an unsupervised method and also unconstrained in terms of pose, illumination, number of persons in the image etc. The goal of this work is to reduce the complexity in terms of time and effort which is generally needed for changing the skin tone using existing applications by professionals or novice. Rigorous experiments shows the efficacy of this method in terms of synthesizing perceptually convincing outputs.

Detail Fusion GAN: High-Quality Translation for Unpaired Images with GAN-Based Data Augmentation

Ling Li, Yaochen Li, Chuan Wu, Hang Dong, Peilin Jiang, Fei Wang

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Auto-TLDR; Data Augmentation with GAN-based Generative Adversarial Network

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Image-to-image translation, a task to learn the mapping relation between two different domains, is a rapid-growing research field in deep learning. Although existing Generative Adversarial Network(GAN)-based methods have achieved decent results in this field, there are still some limitations in generating high-quality images for practical applications (e.g., data augmentation and image inpainting). In this work, we aim to propose a GAN-based network for data augmentation which can generate translated images with more details and less artifacts. The proposed Detail Fusion Generative Adversarial Network(DFGAN) consists of a detail branch, a transfer branch, a filter module, and a reconstruction module. The detail branch is trained by a super-resolution loss and its intermediate features can be used to introduce more details to the transfer branch by the filter module. Extensive evaluations demonstrate that our model generates more satisfactory images against the state-of-the-art approaches for data augmentation.

Local-Global Interactive Network for Face Age Transformation

Jie Song, Ping Wei, Huan Li, Yongchi Zhang, Nanning Zheng

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Auto-TLDR; A Novel Local-Global Interaction Framework for Long-span Face Age Transformation

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Face age transformation, which aims to generate a face image in the past or future, has receiving increasing attention due to its significant application value in some special fields, such as looking for a lost child, tracking criminals and entertainment, etc. Currently, most existing methods mainly focus on unidirectional short-span face aging. In this paper, we propose a novel local-global interaction framework for long-span face age transformation. Firstly, we divide a face image into five independent parts and design a local generative network for each of them to learn the local structure changes of a face image, while we utilize a global generative network to learn the global structure changes. Then we introduce an interactive network and an age classification network, which are respectively used to integrate the local and global features and maintain the corresponding age features in different age groups. Given any face image at a certain age, our network can produce a clear and realistic image of face aging or rejuvenation. We test and evaluate the model on complex datasets, and extensive qualitative comparison experiments has proved the effectiveness and immense potential of our proposed method.

Exemplar Guided Cross-Spectral Face Hallucination Via Mutual Information Disentanglement

Haoxue Wu, Huaibo Huang, Aijing Yu, Jie Cao, Zhen Lei, Ran He

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Auto-TLDR; Exemplar Guided Cross-Spectral Face Hallucination with Structural Representation Learning

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Recently, many Near infrared-visible (NIR-VIS) heterogeneous face recognition (HFR) methods have been proposed in the community. But it remains a challenging problem because of the sensing gap along with large pose variations. In this paper, we propose an Exemplar Guided Cross-Spectral Face Hallucination (EGCH) to reduce the domain discrepancy through disentangled representation learning. For each modality, EGCH contains a spectral encoder as well as a structure encoder to disentangle spectral and structure representation, respectively. It also contains a traditional generator that reconstructs the input from the above two representations, and a structure generator that predicts the facial parsing map from the structure representation. Besides, mutual information minimization and maximization are conducted to boost disentanglement and make representations adequately expressed. Then the translation is built on structure representations between two modalities. Provided with the transformed NIR structure representation and original VIS spectral representation, EGCH is capable to produce high-fidelity VIS images that preserve the topology structure of the input NIR while transfer the spectral information of an arbitrary VIS exemplar. Extensive experiments demonstrate that the proposed method achieves more promising results both qualitatively and quantitatively than the state-of-the-art NIR-VIS methods.