Image Inpainting
277 papers with code • 12 benchmarks • 17 datasets
Image Inpainting is a task of reconstructing missing regions in an image. It is an important problem in computer vision and an essential functionality in many imaging and graphics applications, e.g. object removal, image restoration, manipulation, re-targeting, compositing, and image-based rendering.
Source: High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling
Image source: High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling
Benchmarks
These leaderboards are used to track progress in Image Inpainting
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Libraries
Use these libraries to find Image Inpainting models and implementations
ImageNet
CelebA
FFHQ
Places
CelebA-HQ
ApolloScape
Places365
Fashion-Gen
CASIA V2
FVI
Latest papers with no code
Object Remover Performance Evaluation Methods using Class-wise Object Removal Images
In this letter, to validate the current evaluation methods cannot properly evaluate the performance of an object remover, we create a dataset with object removal ground truth and compare the evaluations made by the current methods using original images to those utilizing object removal ground truth images.
RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting
In this work, we propose an approach for 3D scene inpainting -- the task of coherently replacing parts of the reconstructed scene with desired content.
CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery
In Generalized Category Discovery (GCD), we cluster unlabeled samples of known and novel classes, leveraging a training dataset of known classes.
Terrain Point Cloud Inpainting via Signal Decomposition
The rapid development of 3D acquisition technology has made it possible to obtain point clouds of real-world terrains.
Locate, Assign, Refine: Taming Customized Image Inpainting with Text-Subject Guidance
The process involves (i) Locate: concatenating the noise with masked scene image to achieve precise regional editing, (ii) Assign: employing decoupled cross-attention mechanism to accommodate multi-modal guidance, and (iii) Refine: using a novel RefineNet to supplement subject details.
Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting
We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes.
Fill in the ____ (a Diffusion-based Image Inpainting Pipeline)
Image inpainting is the process of taking an image and generating lost or intentionally occluded portions.
Inpainting-Driven Mask Optimization for Object Removal
In our method, this domain gap is resolved by training the inpainting network with object masks extracted by segmentation, and such object masks are also used in the inference step.
HySim: An Efficient Hybrid Similarity Measure for Patch Matching in Image Inpainting
In this sense, there is still a need for model-driven approaches in case of application constrained with data availability and quality, especially for those related for time series forecasting using image inpainting techniques.
CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility
To this end, this paper proposes a novel text-guided video inpainting model that achieves better consistency, controllability and compatibility.
