Repository: zhang-zx/AVID
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├── LICENSE
└── README.md
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FILE: LICENSE
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MIT License
Copyright (c) 2023 Zhixing Zhang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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FILE: README.md
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# AVID
Any-Length Video Inpainting with Diffusion Model
[](https://arxiv.org/abs/2312.03816)
[](https://zhang-zx.github.io/AVID)
[](https://zhang-zx.github.io/AVID/supp/index.html)
This respository contains the code for the CVPR 2024 paper [AVID: Any-Length Video Inpainting with Diffusion Model](https://arxiv.org/pdf/2312.03816.pdf).
For more visualization results, please check our [supplementary materils](https://zhang-zx.github.io/AVID/supp/index.html).
> **[AVID: Any-Length Video Inpainting with Diffusion Model](https://arxiv.org/abs/2312.03816)** \
> [Zhixing Zhang](https://zhang-zx.github.io/) 1,
> [Bichen Wu](https://scholar.google.com/citations?user=K3QJPdMAAAAJ&hl) 2,
> [Xiaoyan Wang](https://xiaoyan.horizonian.com/) 2,
> [Yaqiao Luo](https://scholar.google.com/citations?user=be4sU3cAAAAJ) 2,
> [Luxin Zhang](https://lucinezhang.github.io/) 2,
> [Yinan Zhao](https://yinan-zhao.github.io/) 2,
> [Peter Vajda](https://sites.google.com/site/vajdap) 2,
> [Dimitris Metaxas](https://people.cs.rutgers.edu/~dnm/) 1,
> and [Licheng Yu](https://lichengunc.github.io/) 2 \
> 1 Rutgers University
> 2 Meta AI
**AVID** is a a video inpainting method versatile across a spectrum of video durations and tasks.
> Recent advances in diffusion models have successfully enabled text-guided image inpainting.
While it seems straightforward to extend such editing capability into video domain, there has 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 level, 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 an 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 range, with high quality.
## Acknowledgement
This codebase is built on top of [diffusers](https://github.com/huggingface/diffusers), [AnimateDiff](https://github.com/guoyww/AnimateDiff), and [Tune-A-Video](https://github.com/showlab/Tune-A-Video).
We thank the authors for their great works.
## Reference
If our work helps you, please consider to cite our paper. Thanks!
```BibTeX
@article{zhang2023avid,
title={AVID: Any-Length Video Inpainting with Diffusion Model},
author={Zhang, Zhixing and Wu, Bichen and Wang, Xiaoyan and Luo, Yaqiao and Zhang, Luxin and Zhao, Yinan and Vajda, Peter and Metaxas, Dimitris and Yu, Licheng},
journal={arXiv preprint arXiv:2312.03816},
year={2023}
}
```
