  Image inpainting is currently a hot topic within the field of computer
vision. It offers a viable solution for various applications, including
photographic restoration, video editing, and medical imaging. Deep learning
advancements, notably convolutional neural networks (CNNs) and generative
adversarial networks (GANs), have significantly enhanced the inpainting task
with an improved capability to fill missing or damaged regions in an image or
video through the incorporation of contextually appropriate details. These
advancements have improved other aspects, including efficiency, information
preservation, and achieving both realistic textures and structures. Recently,
visual transformers have been exploited and offer some improvements to image or
video inpainting. The advent of transformer-based architectures, which were
initially designed for natural language processing, has also been integrated
into computer vision tasks. These methods utilize self-attention mechanisms
that excel in capturing long-range dependencies within data; therefore, they
are particularly effective for tasks requiring a comprehensive understanding of
the global context of an image or video. In this paper, we provide a
comprehensive review of the current image or video inpainting approaches, with
a specific focus on transformer-based techniques, with the goal to highlight
the significant improvements and provide a guideline for new researchers in the
field of image or video inpainting using visual transformers. We categorized
the transformer-based techniques by their architectural configurations, types
of damage, and performance metrics. Furthermore, we present an organized
synthesis of the current challenges, and suggest directions for future research
in the field of image or video inpainting.
