arxiv Preprint – ProPainter: Improving Propagation and Transformer for Video Inpainting

In this episode we discuss ProPainter: Improving Propagation and Transformer for Video Inpainting
by Shangchen Zhou, Chongyi Li, Kelvin C. K. Chan, Chen Change Loy. The paper discusses the limitations of existing approaches to video inpainting, specifically flow-based propagation and spatiotemporal Transformer methods, due to spatial misalignment and limited temporal range. To address these challenges, the authors propose ProPainter, a framework that combines dual-domain propagation with image and feature warping for reliable global correspondences. They also introduce a mask-guided sparse video Transformer to enhance efficiency. ProPainter achieves superior results with a 1.46 dB improvement in PSNR while maintaining efficiency, making it a valuable tool for video inpainting applications.