Fast and Effective Video Inpainting via Implicit Motion-Guided Propagation and Sparse Attention

Video inpainting aims to reconstruct missing or corrupted regions in video frames, with applications in video editing, restoration, and special effects. Current deep video inpainting methods rely on optical flow to guide the propagation of effective features and spatiotemporal attention mechanisms to model relationships between frames. However, as an explicit motion representation, the optical flow extracted offline in preceding steps often suffers from instability and errors during estimation. These errors accumulate during subsequent content hallucination, resulting in artifacts and blurring. Meanwhile, although traditional spatiotemporal attention effectively captures frame relationships, its dense computational nature introduces redundant information, disrupting inpainting tasks and reducing efficiency. To address these issues, we propose an implicit motion-guided approach for efficient video inpainting. Instead of relying on optical flow, our method uses implicit motion in the latent feature space to guide the dual-domain propagation of images and features end-to-end, avoiding error accumulation from the independent optical flow estimation process. Additionally, we introduce a self-correcting module that enables feedback between image and feature propagation, reducing errors during propagation. Furthermore, we design an adaptive sparse video attention mechanism to focus on highly relevant regions, minimizing the impact of irrelevant information. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches both qualitatively and quantitatively, while also delivering superior efficiency.