Flow-based propagation and spatiotemporal Transformer are two mainstream<br>mechanisms in video inpainting (VI). Despite the effectiveness of these<br>components, they still suffer from some limitations that affect their<br>performance. Previous propagation-based approaches are performed separately<br>either in the image or feature domain. Global image propagation isolated from<br>learning may cause spatial misalignment due to inaccurate optical flow.<br>Moreover, memory or computational constraints limit the temporal range of<br>feature propagation and video Transformer, preventing exploration of<br>correspondence information from distant frames. To address these issues, we<br>propose an improved framework, called ProPainter, which involves enhanced<br>ProPagation and an efficient Transformer. Specifically, we introduce<br>dual-domain propagation that combines the advantages of image and feature<br>warping, exploiting global correspondences reliably. We also propose a<br>mask-guided sparse video Transformer, which achieves high efficiency by<br>discarding unnecessary and redundant tokens. With these components, ProPainter<br>outperforms prior arts by a large margin of 1.46 dB in PSNR while maintaining<br>appealing efficiency.