Video Inpainting

Video inpainting aims to intelligently fill missing or corrupted regions in video sequences, restoring visual integrity and temporal consistency. Current research heavily utilizes deep learning, focusing on diffusion models, transformers, and flow-based methods to achieve high-quality inpainting, often incorporating techniques like optical flow estimation and depth information for improved accuracy. These advancements are significant for applications ranging from video restoration and editing to medical imaging and enhancing the quality of endoscopic procedures, impacting both scientific understanding of image processing and practical applications across various fields.