Flow-based propagation and spatiotemporal Transformer are two mainstreammechanisms in video inpainting (VI). Despite the effectiveness of thesecomponents, they still suffer from some limitations that affect theirperformance. Previous propagation-based approaches are performed separatelyeither in the image or feature domain. Global image propagation isolated fromlearning may cause spatial misalignment due to inaccurate optical flow.Moreover, memory or computational constraints limit the temporal range offeature propagation and video Transformer, preventing exploration ofcorrespondence information from distant frames. To address these issues, wepropose an improved framework, called ProPainter, which involves enhancedProPagation and an efficient Transformer. Specifically, we introducedual-domain propagation that combines the advantages of image and featurewarping, exploiting global correspondences reliably. We also propose amask-guided sparse video Transformer, which achieves high efficiency bydiscarding unnecessary and redundant tokens. With these components, ProPainteroutperforms prior arts by a large margin of 1.46 dB in PSNR while maintainingappealing efficiency.