Image Outpainting
10 papers with code • 1 benchmarks • 1 datasets
Predicting the visual context of an image beyond its boundary.
Benchmarks
These leaderboards are used to track progress in Image Outpainting
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Places365
Most implemented papers
Painting Outside the Box: Image Outpainting with GANs
ShinyCode/image-outpainting
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The challenging task of image outpainting (extrapolation) has received comparatively little attention in relation to its cousin, image inpainting (completion).
Wide-Context Semantic Image Extrapolation
shepnerd/outpainting_srn
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CVPR 2019
This paper studies the fundamental problem of extrapolating visual context using deep generative models, i. e., extending image borders with plausible structure and details.
Image Outpainting and Harmonization using Generative Adversarial Networks
basilevh/image-outpainting
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This way, the hallucinated details are integrated with the style of the original image, in an attempt to further boost the quality of the result and possibly allow for arbitrary output resolutions to be supported.
Multimodal Image Outpainting With Regularized Normalized Diversification
In this paper, we study the problem of generating a set ofrealistic and diverse backgrounds when given only a smallforeground region.
Very Long Natural Scenery Image Prediction by Outpainting
z-x-yang/NS-Outpainting
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ICCV 2019
The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input.
Enhanced Residual Networks for Context-based Image Outpainting
Although humans perform well at predicting what exists beyond the boundaries of an image, deep models struggle to understand context and extrapolation through retained information.
SiENet: Siamese Expansion Network for Image Extrapolation
nanjingxiaobawang/SieNet-Image-extrapolation
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In this paper, a novel two-stage siamese adversarial model for image extrapolation, named Siamese Expansion Network (SiENet) is proposed.
Bridging the Visual Gap: Wide-Range Image Blending
julia0607/Wide-Range-Image-Blending
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CVPR 2021
In this paper we propose a new problem scenario in image processing, wide-range image blending, which aims to smoothly merge two different input photos into a panorama by generating novel image content for the intermediate region between them.
In-N-Out: Towards Good Initialization for Inpainting and Outpainting
Our self-supervision method, In-N-Out, is summarized as a training approach that leverages the knowledge of the opposite task into the target model.
Cylin-Painting: Seamless 360° Panoramic Image Outpainting and Beyond with Cylinder-Style Convolutions
Motivated by this analysis, we present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting and efficiently fuses the different arrangements, with a view to leveraging their complementary benefits on a consistent and seamless cylinder.
