Recent studies have shown the importance of modeling long-range interactions in the
inpainting problem. To achieve this goal, existing approaches exploit either standalone …

Modern image inpainting systems, despite the significant progress, often struggle with large
missing areas, complex geometric structures, and high-resolution images. We find that one …

Image inpainting has made remarkable progress with recent advances in deep learning.
Popular networks mainly follow an encoder-decoder architecture (sometimes with skip …

The most image inpainting algorithms often have existed problems such as blurred image,
texture distortion and semantic inaccuracy, and the image inpainting effect is limited for …

Recently proposed image inpainting method LaMa builds its network upon Fast Fourier
Convolution (FFC), which was originally proposed for high-level vision tasks like image …

Image inpainting is generally done using either a domain-specific (preconditioned) model or
a generic model that is postconditioned at inference time. Preconditioned models are fast at …

Flow matching is a recent framework to train generative models that exhibits impressive
empirical performance while being relatively easier to train compared with diffusion-based …

Our everyday lives are filled with occlusions that we strive to see through. By aggregating
desired background information from different viewpoints, we can easily eliminate such …

The purpose of image inpainting is to recover scratches and damaged areas using context
information from remaining parts. In recent years, with the development of convolutional …

Image completion with large-scale free-form missing regions is one of the most challenging
tasks for the computer vision community. While researchers pursue better solutions …