The field of image inpainting is witnessing significant advancements driven by innovations in diffusion models and their applications. Recent developments focus on enhancing the quality and diversity of inpainted images, addressing semantic consistency, and improving the efficiency of the inpainting process. Techniques such as variational inference, anisotropic Gaussian splatting, and feature dequantization are being employed to refine the structural integrity and detail of inpainted images. Additionally, methods for controlling latent diffusion models and optimizing inference speed through parallel processing are emerging as key areas of innovation. These advancements not only improve the visual plausibility of inpainted content but also expand the practical applications of inpainting technologies, such as in interactive and real-time scenarios. Notably, the integration of multimodal large language models for generating inpainting prompts and the development of adaptive inpainting methods that consider user input habits are pushing the boundaries of what is possible in this domain.

Noteworthy Papers:

• A hierarchical variational inference algorithm significantly outperforms previous approaches in image inpainting, offering both plausibility and diversity.
• A novel method combining diffusion models with anisotropic Gaussian splatting produces high-fidelity inpainting results with enhanced structural integrity.
• An efficient training method for feature dequantization significantly enhances detail quality in generated images with minimal overhead.