Image Inpainting
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Greatest papers with code
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
• aymericdamien/TopDeepLearning
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The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori.
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input.
Semantic Image Inpainting with Deep Generative Models
CVPR 2017
• bamos/dcgan-completion.tensorflow
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In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data.
Free-Form Image Inpainting with Gated Convolution
• JiahuiYu/generative_inpainting
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We present a novel deep learning based image inpainting system to complete images with free-form masks and inputs.
Generative Image Inpainting with Contextual Attention
CVPR 2018
• JiahuiYu/generative_inpainting
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Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions.
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
CVPR 2017
• leehomyc/Faster-High-Res-Neural-Inpainting
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Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal.
Image Inpainting for Irregular Holes Using Partial Convolutions
ECCV 2018
• MathiasGruber/PConv-Keras
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Existing deep learning based image inpainting methods use a standard convolutional network over the corrupted image, using convolutional filter responses conditioned on both valid pixels as well as the substitute values in the masked holes (typically the mean value).
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
CVPR 2017 • Evolving-AI-Lab/ppgn
PPGNs are composed of 1) a generator network G that is capable of drawing a wide range of image types and 2) a replaceable "condition" network C that tells the generator what to draw.
Pluralistic Image Completion
CVPR 2019
• lyndonzheng/Pluralistic-Inpainting
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In this paper, we present an approach for \textbf{pluralistic image completion} -- the task of generating multiple and diverse plausible solutions for image completion.
Deep Video Inpainting
CVPR 2019
• mcahny/Deep-Video-Inpainting
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Video inpainting aims to fill spatio-temporal holes with plausible content in a video.
IMAGE INPAINTING VIDEO DENOISING VIDEO INPAINTING VIDEO-TO-VIDEO SYNTHESIS