Image Inpainting

Image inpainting aims to intelligently fill in missing or damaged regions of images, restoring visual integrity and enabling various creative and practical applications. Current research emphasizes improving the control and quality of inpainting, particularly through the use of diffusion models and generative adversarial networks (GANs), often incorporating auxiliary information like text prompts, reference images, or depth maps to guide the inpainting process. These advancements are driving progress in areas such as image editing, video processing, and robotic manipulation, with applications ranging from restoring damaged artwork to enhancing augmented reality experiences. The field is also seeing efforts to improve the efficiency and robustness of inpainting algorithms, addressing challenges like handling diverse mask types and achieving high-fidelity results.