Image Inpainting
State-of-the-art leaderboards
Greatest papers with code
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.
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
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.
Semantic Image Inpainting with Deep Generative Models
CVPR 2017
• bamos/dcgan-completion.tensorflow
• 
In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data.
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
CVPR 2017
• leehomyc/Faster-High-Res-Neural-Inpainting
• 
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.
Free-Form Image Inpainting with Gated Convolution
• JiahuiYu/generative_inpainting
•
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
•
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.
Image Inpainting for Irregular Holes Using Partial Convolutions
ECCV 2018
• MathiasGruber/PConv-Keras
• 
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).
Pluralistic Image Completion
CVPR 2019
• lyndonzheng/Pluralistic-Inpainting
•
In this paper, we present an approach for \textbf{pluralistic image completion} -- the task of generating multiple and diverse plausible solutions for image completion.
Generative Modeling by Estimating Gradients of the Data Distribution
• ermongroup/ncsn
•
We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching.
SOTA for Image Generation on CIFAR-10
Shift-Net: Image Inpainting via Deep Feature Rearrangement
ECCV 2018
• Zhaoyi-Yan/Shift-Net
•
To this end, the encoder feature of the known region is shifted to serve as an estimation of the missing parts.