Image to Sequence: Captioning the Image with Transfer Learning. (Kasmi et Thimonier)(CS et GAN) Compressed Sensing using Generative Models Modeling Sparse Deviations for CS using GAN 2. Free-Form Image Inpainting with Gated Convolution Jiahui Yu1 Zhe Lin2 Jimei Yang2 Xiaohui Shen3 Xin Lu2 Thomas Huang1 1University of Illinois at Urbana-Champaign 2Adobe Research 3ByteDance AI Lab Figure 1: Free-form image inpainting results by our system built on gated convolution. View in Colab • GitHub source Generative adversarial networks (GANs) have been extensively studied in the past few years. by Junyang Hu, Wentao Cui 10. Improved GAN by Salimans et al. The two most significant are … Contextual-based Image Inpainting: Infer, Match, and Translate 5 3.2 System Overview Our system divides the image inpainting tasks into three steps: Inference:We use an Image2Feature network to fill an incomplete image with coarse contents as inference and extract a feature map from the inpainted image. GANs. Image completion. Semantic Image Completion and Enhancement using Deep Learning. In the past few years, Generative Adversarial Networks (GANs) have dramatically advanced our ability to represent and parameterize high-dimensional, non-linear image manifolds. 5.6. Recently, algorithms have been introduced for image style transfer using generative models like Generative Ad-versarial Networks [4] (GAN) and Variational Auto En- Image completion using deep convolutional generative adversarial nets in tensorflow. It could be that we want to remove parts of the image e.g. Abstract Image completion is a useful tool for image editing. This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN). [Salimans et al. Abstract. An actionable, real-world course on OpenCV and computer vision. Now split the dataset into “HerX.npy” with all the image data and “HerY.npy” with all the labels for training. As reported by Cisco, 90% of net traffic will be visual, and indeed, most of the visual data are cat photos and videos. Am recognized innovator with 63 patents. UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition : 2018: CVPR 2018: Disentangling Structure and Aesthetics for Style-aware Image Completion : 2018: ECCV 2018: Image Inpainting for Irregular Holes Using Partial Convolutions : 2018: ECCV 2018: Contextual-based Image Inpainting: Infer, Match, and Translate : 2018 Rolf et al. Image completion and inpainting are closely related technologies used to fill in missing or corrupted parts of images. In addition, several methods have been proposed using GAN’s architecture in order to transform an input into a desired shape and improve the fusion performance , , , . Inside PyImageSearch Gurus, you'll find:. Sketch2Color anime GAN is a supervised learning model i.e given a black-and-white sketch it can generate a colored image based on the sketch-color image pairs used in the training data. The completion network is fully convolutional and used to complete the image, while both the global and the local context discrimina- Generative Adversarial Network (GAN) is a class of deep learning models fascinating even to deep learning researchers and engineers. in SIGGRAPH, 2003; Scene Completion using Millions of Photographs, Hays et al. Therefore, using scope is important here. Song, L. Gao, F. Nie, H. Shen, Y. Yan, N. Sebe IEEE Transactions on Image Processing, 2016 This process is shown in Fig.5. I apply innovative technologies like machine learning, computer vision, and physics to further an organization's goals. We train a generated adversarial network, i.e, contextual GAN to learn the joint distribution of sketch and the corresponding image by using joint images. ️ means implementation is available. 01 learning dcspns with differentiable mpe relationship with gans ... scores in image completion and nice surprises including variability in image sampling and an intriguing relationship with gan. tween the translation from one image style to another and back. Visual Studio Code: How Microsoft's 'any OS, any programming language, any software' plan is paying off. Important Links. Our work employs GANs for representa- Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today. The generated image appears to share characteristics of both images. Style Transfer using CNN (CVPR 2016) Perceptual Loss (ECCV 2016) More paper; Colorization. This code is based on a paper titled Image Completion Using Efficient Belief Propagation via Priority Scheduling and Dynamic Pruning, by Nikos Komodakis and Georgios Tziritas. Interactive image completion with perspective correction. Here’s a tutorial on how to develop a DCGAN model in TensorFlow 2.0. The first is exemplar-based approach and we consider PatchMatch-based inpainting for this study. Author: fchollet Date created: 2019/04/29 Last modified: 2021/01/01 Description: A simple DCGAN trained using fit() by overriding train_step on CelebA images. Iizuka et al. State-of-the-art GANs, such as StyleGAN2[27] and BigGAN[4], can generate extremely realistic images at high resolution. The completed image. MIC-GAN: multi-view assisted image completion using conditional generative adversarial networks. PyImageSearch Gurus has one goal.....to make developers, researchers, and students like yourself become awesome at solving real-world computer vision problems.. GAN based techniques such as Image-to-Image translations, DeepFakes, and other automated ... Due to the success of using GANs for image editing, it ... super-resolution and image completion. 0 In 2019, DeepMind showed that variational autoencoders (VAEs) could outperform GANs on face generation. J. Introduction. View in Colab • GitHub source A curated list of GAN & Deepfake papers and repositories. The authors proved the effectiveness of their model through a comprehensive set of experiments. of Info and … The other is the data-driven approach and inpainting using a million images is considered for this study. The code is written using the Keras Sequential API with a tf.GradientTape training loop.. What are GANs? Training procedure is a bit different from the one described in the paper. Zhou Chuansai, Towards Robust Image Classification Using Sequential Attention Models, CVPR 2020. 5–10 (2016) Google Scholar There is work on using GANs to generate adversarial examples, but this is not quite the same thing. Specifying Object Attributes and Relations in Interactive Scene Generation. The edge gen-erator hallucinates edges of the missing region (both reg-ular and irregular) of the image, and the image comple-tion network fills in the missing regions using hallucinated edges as a priori. One such possibility is using separate complementary halves from two images as input and edge map respectively. Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (ICLR 2021 Spotlight) ... and propose a novel GAN architecture that bridges the gap between image-conditional and unconditional generators, which enables it to generate very convincing complete images from inputs with large portions masked out. Completion network is trained, then the completion network is fixed and discriminators are trained, then finally both are trained. Given a mask, a network is trained to predict the content in the mask. We train a generated adversarial network, i.e, contextual GAN to learn the joint distribution of sketch and the corresponding image by using joint images. Poisson Image Editing, Pérez et al. success especially in the field of computer vision e.g., plausible image generation [30]–[34], image-to-image translation [2], [35]–[41], image super-resolution [26], [42]–[45] and image completion [46]–[50]. using the ADADELTA optimizer • Batch composition: care for sufficient randomness in the presentation order • Regularization: use dropout to overcome the problem of more parameters then input data • Learning rate: choose adaptive learning rates, e.g. the difference between image mixing using the encoder and interpolation in latent space. UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition : 2018: CVPR 2018: Disentangling Structure and Aesthetics for Style-aware Image Completion : 2018: ECCV 2018: Image Inpainting for Irregular Holes Using Partial Convolutions : 2018: ECCV 2018: Contextual-based Image Inpainting: Infer, Match, and Translate : 2018 Image style transfer using convolutional neural networks, Gatys et al., CVPR 2016. Although both of the two stages also introduce the generative adversarial network (GAN) [3], this paper put more attention to the enhancement of the generator model rather than the discriminator model. E x is the expected value over all real data instances. GitHub is where people build software. Awesome-GANS-and-Deepfakes. ~/GAN/gantut_trainer.py --dataset ~/GAN/aligned --epoch 20 Of course, if you’ve put your aligned training set somewhere else, make sure that path goes into the --dataset flag. Check this link to run streamlit on Colab. Click To Get Model/Code. Currently, GANs are best in class in image synthesis and have been applied to many other tasks such as image completion [38] and image-to-image translation [25,61]. Context Aware Image Inpainting aims to fill a region in an image based on the context of surrounding pixels. The proposed method also shows a superior TEC completion performance over the traditional single-image inpainting methods such as TELEA and NS using the realistic MIT-TEC masks and ten-fold cross-validation. Read writing from AI News Clips by Morris Lee: News to help your R&D on Medium. Object Detection, Intel Korea (Mar. [19]. Both stages follow an adversarial framework [19] to ensure that the hallucinated edges and the RGB pixel intensities We will now take 64 neurons as input and spits out an image with another dense layer with sigmoid activation function. Merge the image with the identifier using numpy and shuffle the data. Image Completion refers to the task of filling in the missing regions of an image and Image Extrapolation refers to the task of extending an image at its boundaries while keeping it coherent. disentangles structure inference and content completion ex-plicitly. Hong Tao, Noise2Noise: Learning Image Restoration without Clean Data, ICML 2018 / Noise2Void - Learning Denoising from Single Noisy Images, CVPR 2019. The Conditional Analogy GAN: Swapping Fashion Articles on People Images (link) Given three input images: human wearing cloth A, stand alone cloth A and stand alone cloth B, the Conditional Analogy GAN (CAGAN) generates a human image wearing cloth B. MIC-GAN: Multi-view Assisted Image Completion Using Conditional Generative Adversarial Networks Gagan Kanojia Electrical Engineering Indian Institute of Technology Gandhinagar Gandhinagar, Gujarat, India gagan.kanojia@iitgn.ac.in Shanmuganathan Raman Electrical Engineering Indian Institute of Technology Gandhinagar Gandhinagar, Gujarat, India The input to the generator is updated using SGD so that the generated image (image B) resembles image A in the known part. The image completion then estimates RGB intensities of the region using hallucinated edges. Intelligence Test Using Generative Image Inpainting Techniques as the first author to target CogSci ... • Reproduced the experimental results of a paper on face completion with Generative Adversarial Networks (GAN) • Tested whether unpaired geometry -face datasets would lead to good quality synthesized face ... We use a latent regressor network that learns from missing data for image composition and image completion. GAN Tutorial (NeurIPS 2016), by Ian Goodfellow. Rolf et al. Pix2pix is an extension of a traditional GAN called a “Conditional GAN”, in which the generator is fed an input image in addition to noise. We propose to study two different approaches to address this problem. The left image is the source image required to be extended and the right image is the extended image. We propose to extend their work by changing the objective function to incorporate better image generation. Given a single view image as input, Multi-view GAN produces a unified rep- resentation which consists of a 3D shape descriptor and a view descriptor. The image completion network is then trained to fool the both context discriminator networks, which requires it to generate images that are indistinguishable from real ones with regard to overall consistency as well as in details. ️ means implementation is available. no training code] [tensorflow implementation. are available here. (Puyrazat et Belemkoabga)(Deep … We evaluate our model end-to-end over the publicly available datasets CelebA, Places2, and Paris This is a tensorflow implementation of the paper Globally and Locally Consistent Image Completion. 9. CVPR 2018 Tutorial on GANs. This motivates us to tackle image completion using a deep learning framework. Context-Aware Image Inpainting using Deep Convolutional Generative Adversarial Networks. We will just use one dense layer as hidden layer which takes latent variable z as the input and maps to 64 neurons. by Hengda He, Jiawei Ma, Xiaoxiao Yan 11. In this paper, a deep learning architecture is proposed to deal with image completion and enhancement. Depth Completion. Deep Learning based Diffeomorphic Medical Image Registration for fMRI Studies. Why are generative models important? Degree in Computer Science (09/2015 - 07/2020) Wangxuan Institute of Computer Technology, Peking University Bachelor’s Degree in Computer Science (09/2010 - 07/2015) School of Electronics Engineering and Computer Science, Peking University GANs. Contribute to AdityaVallabh/Image-Inpainting-GAN development by creating an account on GitHub. II. Faceshop [25] is a face editing system that takes Pathak et al. Based on this discovery, we devised PixelDefend, a new approach that purifies a maliciously perturbed image by moving it back towards the distribution seen in the training data. Motivation. CVAE-GAN - CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training CycleGAN - Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks ( github ) D-GAN - Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data A curated list of GAN & Deepfake papers and repositories. This is where part of a scene may be missing and we wish to recover the full image. Given a few standard image datasets, the model was trained to create new images Test set data was compared to a Gaussian Parzen window fit to generated samples using log likelihood See paper for more generated images Image from Experiments section of article network to obtain complete synthesized parsing. For single image based modeling, the representation can More than 65 million people use GitHub to discover, fork, and contribute to over 200 million projects. However, GANs are not without problems. Image Completion using GAN. Whereas a traditional GAN merely generates images in a target domain, this allows a conditional GAN to translate between two domains (in our case, from k … See figures below. - Ranked 1st out of 7 in Autonomous Driving: High Solution Scene Depth Prediction based on LIDAR and video, using the self-supervised depth-completion model published in ICRA 2019 Creative Software Applications Contest Honorable Mention , Dept. CVPR 2016. The work by [15] propose a model based on the CVAE framework. 2.1. Pluralistic Image Completion. Simultaneous inpainting for image structure and texture using anisotropic heat transfer model. Research fellow (10/2020 - now) School of Computer Science and Engineering, Nanyang Technological University Ph.D. Edge generation is solely focused on hallucinating edges in the missing regions. Image Extension with Contour Patch Matching and Generative Adversarial Network Xiaochen Zhou zhouxiaochen@wustl.edu Figure 1: Image extension results by our approach. DCGAN to generate face images. Hence for ‘-‘ image, the folder it is contained in is named ‘-‘. We show that our approach can be used to complete a wide variety of scenes. Google Scholar; Chuan Qin, Shuozhong Wang, and Xinpeng Zhang. In this project, we explore the problem of semantic image completion. We fill in large missing areas in the depth channel of an RGB-D image by predicting normals from color and then solving for completed depths. Training with EEG information is visualized in the “Flow 2” of Fig. That is like what this conditional GAN do, it can image a relative part, but the specific structures may rely on high level abstract knowledge. The same regressor setup can be used for a variety of other image editing applications, such as multimodal editing, scene completion, or dataset rebalancing. We present a novel algorithm for self-supervised monocular depth completion in … Sketch2Color anime GAN is a supervised learning model i.e given a black-and-white sketch it can generate a colored image based on the sketch-color image pairs used in the training data. Gagan Kanojia, Shanmuganathan Raman National Conference on Communications (NCC),2020 Semantic Image Inpainting with Deep Generative Models Raymond A. Yeh, Chen Chen, Teck Yian Lim, Alexander G. Schwing, Mark Hasegawa-Johnson, Minh N. Do University of Illinois at Urbana-Champaign fyeh17, cchen156, tlim11, aschwing, jhasegaw, minhdog@illinois.edu Abstract Semantic image inpainting is a challenging task where the shape using 3DMM and perform preliminary completion using GAN. In the past, many classical approaches had been proposed but were not satisfying. The whole network of FCSR-GAN can be trained end-to-end using our two-stage training strategy. Numerical evaluations using IoU and mAP are described below.-Generation from normal distribution Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes PENG-SHUAI WANG and CHUN-YU SUN, Tsinghua University and Microsoft Research Asia YANG LIU and XIN TONG, Microsoft Research Asia Fig. Our Adaptive O-CNN is capable of generating high-quality planar-patch-based shapes from a single image as shown above (odd columns: input images; Recent Related Work Generative adversarial networks have been vigorously explored in the last two years, and many conditional variants have been proposed. ICCV 2019 , source cdode, Generating image using scene-text-grapth , Youtube GUI. Tl;dr GANs containg two competing neural networks which iteratively generate new data with the same statistics as the training set. [ML-Heavy] Generative Adversarial Net (GAN) building blocks Using to produce fake images [ML-Heavy] Training DCGANs Existing GAN and DCGAN implementations [ML-Heavy] DCGANs in TensorFlow Running DCGAN on your images Step 3: Finding the best fake image for image completion Image completion with DCGANs [ML-Heavy] Loss function for projecting onto locally and globally consistent natural image completion. Inside the loop, we arrange each original and recon image side-by-side, vertically stacking all results as an outputs image. The outputs of this network are 2.5D images and comprises of Depth image, Surface Normal Image and a Silhouette Image. Stereo Magnification; 3D Photo Inpant; Dynamic Scenes View; Style Transfer. face or image completion and super-resolution approaches to build an end-to-end recovering model; (iv) we have provided comprehensive review about related work and more details and evaluations of our FCSR-GAN. zi2zi is the follow-up work for my last project, once again tackling the same problem of style transfer between Chinese fonts.. ... Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. You can check at each step how the transformer is being trained from decoder output to get the final image. In a surreal turn, Christie’s sold a portrait for $432,000 that had been generated by a GAN, based on open-source code written by Robbie Barrat of Stanford.Like most true artists, he didn’t see any of the money, which instead went to the French company, Obvious. And, as existing single image-to-image … We study the problem of 3D object generation. Using statistical hypothesis testing, we find that modern neural density models are surprisingly good at detecting imperceptible image perturbations. (Figure 1): edge generation and image completion. We will expand on using VAEs for image generation and completion using the CelebA dataset [9]. Using latent space regression to analyze and leverage compositionality in GANs. What is an Autoencoder? GAN use cases Research is just starting to gain momentum; we expect more to see in the future • Generate images from text Reed et al., «Generative Adversarial Text to Image Synthesis», 2016 • Segment images into semantically meaningful parts Luc et al., «Semantic Segmentation using Adversarial Networks», 2016 Yang et al. ∙ SAMSUNG ∙ 11 ∙ share . Recently, deep learning and GAN-based approaches have been utilized to tackle image completion problem and show decent results. to do ‘inpainting’ or ‘image completion’. In: Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, pp. For the example in the post, I completed face images that are missing the center portion. FET-GAN: Font and Effect Transfer via K-shot Adaptive Instance Normalization. Recently, deep learning and GAN-based approaches have been utilized to tackle image completion problem and show decent results. Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks. [37] further improve inpainting with a Problem Statement – Enhance Image Resolution using Autoencoder; Implementing an Autoencoder using Python . Our ar-chitecture is composed of three networks: a completion network, a global context discriminator, and a local context discriminator. After that, we attach the completed UV texture to the shape … PyTorch implements a simple GAN neural network structure. disentangles structure inference and content completion ex-plicitly. Optimized Graph Learning Using Partial Tags and Multiple Features for Image and Video Annotation. image synthesis [16]. This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch.Feel free to make a pull request to contribute to this list. completion task, after extracting a style feature from a de-sired image, we combine the style feature while changing the content features of same desire images. ICCV 2017 Tutorial on GANs. However, most existing methods cannot control the contents of the generated video using a text caption, losing their usefulness to a large extent. Image completion. Image completion Why are generative models important? Detecting objects using TensorFlow API; Converting TensorFlow pb file to OpenVINO xml and bin file; Image Classification, Intel Korea (Nov. 2019 ~ Feb. 2020) GitHub. Model the probability density of images Understanding P(X) may help us understand P(Y | X) Generate novel content Generate training data for discriminative networks Artistic applications Image completion Monte-carlo estimators 3 4. Faceshop [25] is a face editing system that takes 2016] employed feature matching and minibatch discrimination to improve the output. deep convolutional sum-product Two models are trained simultaneously … Then, incomplete image, composed mask, and synthesized parsing are fed into parsing-aware inpainting network for manipulating the image by using the sketch and color. Image Generation from Sketch Constraint Using Contextual GAN Edit social preview ... results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers. However, Darko Pavic, Volker Schönefeld, and Leif Kobbelt. D(G(z)) is the discriminator's estimate of the probability that a fake instance is real. 0519: A Simple Baseline for Multi-Object Tracking sis that maps the image, label, and the semantics into the same latent space. It draws attention in mainstream media such as deep fake images and videos, aging apps, and beautification apps. Metric learning [19] learns a discrimi-native metric to measure the image/label similarity. people, and fill-in the background. (GANs) [19]. We propose a novel framework, namely 3D Generative Adversarial Network (3D-GAN), which generates 3D objects from a probabilistic space by leveraging recent advances in volumetric convolutional networks … Structure-Guided Image Completion via Regularity Statistics ICASSP 2016 Shuai Yang, Jiaying Liu, Sijie Song, Mading Li and Zongming Guo In this paper, we propose a novel hierarchical image completion approach using regularity statistics, considering structure features. Tl;dr GANs containg two competing neural networks which iteratively generate new data with the same statistics as the training set. 256 x 256 x 3 [official torch implementation. Model the probability density of images Understanding P(X) may help us understand P(Y | X) Generate novel content Generate training data for discriminative networks Artistic applications Image completion Monte-carlo estimators 3 4. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation, and similar domains. In the last few years, we see AI is reaching a productivity plateau in the field of content generation. The faces received from the final result will be blurry and low res because the model is trained for an 64x64 image … The pipeline of the proposed approach. [Iizuka et al. I just released a blog post on image completion that some of you might be interested in. GitHub Gist: star and fork mithunkamat's gists by creating an account on GitHub. We address this problem using a novel joint image completion approach, where the sketch provides the image context for completing, or generating the output image. in TOG, 2007; CG2Real: Improving the Realism of Computer Generated Images using a Large Collection of Photographs, Johnson et al. Related code can be found here. An intuitive solution to SIC is treating stereo pairs as doubled single images and repairing them one by one using methods for single image completion. 2017] presented a GAN-based method for image completion, and used a Dilated Convolution implementation in their completion … Two-phase Hair Image Synthesis by Self-Enhancing Generative Model arXiv_CV arXiv_CV Adversarial Super_Resolution Sparse GAN; 2019-02-28 Thu. The field of automatic video generation has received a boost thanks to the recent Generative Adversarial Networks (GANs). Conditional Deep Convolutional GANs for Image Completion… [8] exploit the shape information of the missing regions and train a neuron network for denoising … Wasserstein GAN architecture of the GANs is used for image completion which recovers the missing regions by filling the corrupted part in the damaged image, and the enhancement network will further refine the completed images using residual learning techniques and then provide a better quality image as output. More than 65 million people use GitHub to discover, fork, and contribute to over 200 million projects. (MADALI NABIL et cochet)(Deep learning) Convex relaxation of conv net convex relaxation of veri cation 3. has UI] Both the models are trained using backpropagation and dropout algorithms and samples obtained from the generator only using forward propagation. In this function: D(x) is the discriminator's estimate of the probability that real data instance x is real. A deep learning approach to universal image manipulation detection using a new convolutional layer. This particularly affects human videos due to their great variety of actions and appearances. More than 56 million people use GitHub to discover, fork, and contribute to over 100 million projects. The results of reconstruction using encoder and generator and refiner. SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee Accepted to ICRA, 2021 arXiv. Multim. In my experiment, CAGAN was able to swap clothes in different categories,… Figure 1: Network Flow Chart (source: KDnuggets - … By experimenting on the fixed low-rank matrix completion, collaborative filtering, and image and video recovery, it illustrates that the proposed method is superior to the state-of-the-art methods on the convergence efficiency and the numerical performance. The other flags can be set to default because that’s how we’ve written our GAN class. There are two components in a GAN which try to work against each other (hence the ‘adversarial’ part). Globally and Locally Consistent Image Completion; Partial Convolutions; Image synthesis. Contextual-based Image Inpainting: Infer, Match, and Translate 5 3.2 System Overview Our system divides the image inpainting tasks into three steps: Inference:We use an Image2Feature network to fill an incomplete image with coarse contents as inference and extract a feature map from the inpainted image. Although single image completion in deep learning framework has been studied for years [2, 9, 10], there are seldom deep-learning based stereo image completion methods. DCGAN to generate face images. Diffusion-based image completion methods 1 – 4 are based on partial differential equations (PDE) in which a diffusive process is modeled using PDE to propagate colors into the missing regions. It is a pleasant surprise that the Rewrite project gets a fair amount of attention and interests, however, looking back, the result feels underwhelming. 3D Point Cloud Completion using Latent Optimization in GANs Shubham Agarwal*, Swaminathan Gurumurthy* WACV 2019 [3] We address a fundamental problem with Neural Network based point cloud completion methods which reconstruct the entire structure rather than preserving the points already provided as input. The Visual Computer 22, 9-11 (2006), 671--681. ... [10] Jampani et al. GAN Based Image Deblurring Using Dark Channel Prior arXiv_CV arXiv_CV Adversarial GAN Quantitative; 2019-02-28 Thu. Then, incomplete image, composed mask, and synthesized parsing are fed into parsing-aware inpainting network for manipulating the image by using the sketch and color.
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