Advances in Image Inpainting: Global Context Modeling via Transformers and Diffusion Models

Abstract

Image inpainting, a critical task in computer vision, has significantly benefited from the rapid development of deep learning techniques, particularly Transformers and Diffusion Models. Traditional methods relying on texture matching and PDE-based diffusion strategies demonstrate limited effectiveness in complex or extensive damaged regions. Recent advancements employing Transformer architectures effectively exploit global context via self-attention mechanisms, ensuring structural coherence in large missing areas. Hybrid models integrating transformers and convolutional networks, such as MAT, further enhance performance by combining global semantic understanding and local detail restoration. Meanwhile, diffusion Models, through iterative denoising steps, offer substantial improvements in realism and texture fidelity, outperforming previous methods in generating high-quality, diverse inpainting outcomes. Despite these achievements, challenges remain concerning computational efficiency, training complexity, and generalization to irregular and extensive missing regions. Future research directions identified include improving model efficiency for ultra-high-resolution tasks, strengthening global semantic coherence by incorporating vision-language priors, enhancing user controllability via multi-modal inputs, and developing better perceptual evaluation metrics. This paper systematically reviews state-of-the-art Transformer-based and Diffusion-based methods, analyzes their strengths and limitations, and outlines critical areas for further advancement, providing valuable insights for ongoing research in image inpainting.