AVID: Any-Length Video Inpainting with Diffusion Model

TL;DR

AVID introduces a diffusion-based model capable of performing high-quality, flexible, text-guided video inpainting across variable lengths, addressing key challenges like temporal consistency and structural fidelity.

Contribution

The paper presents AVID, a novel diffusion model with motion modules and multi-diffusion sampling, enabling any-length, high-quality video inpainting guided by text prompts.

Findings

Robust performance across various inpainting types

Effective handling of different video durations

High visual quality in generated videos

Abstract

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/ .