  Recent advances in diffusion models have successfully enabled text-guided
image inpainting. While it seems straightforward to extend such editing
capability into the video domain, there have been fewer works regarding
text-guided video inpainting. Given a video, a masked region at its initial
frame, and an editing prompt, it requires a model to do infilling at each frame
following the editing guidance while keeping the out-of-mask region intact.
There are three main challenges in text-guided video inpainting: ($i$) temporal
consistency of the edited video, ($ii$) supporting different inpainting types
at different structural fidelity levels, and ($iii$) dealing with variable
video length. To address these challenges, we introduce Any-Length Video
Inpainting with Diffusion Model, dubbed as AVID. At its core, our model is
equipped with effective motion modules and adjustable structure guidance, for
fixed-length video inpainting. Building on top of that, we propose a novel
Temporal MultiDiffusion sampling pipeline with a middle-frame attention
guidance mechanism, facilitating the generation of videos with any desired
duration. Our comprehensive experiments show our model can robustly deal with
various inpainting types at different video duration ranges, with high quality.
More visualization results are made publicly available at
https://zhang-zx.github.io/AVID/ .
