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license: mit |
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# [ECCV 2022] Flow-Guided Transformer for Video Inpainting |
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[](https://github.com/hitachinsk/FGT/blob/main/LICENSE) |
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### [[Paper](https://arxiv.org/abs/2208.06768)] / [[Codes](https://github.com/hitachinsk/FGT)] / [[Demo](https://youtu.be/BC32n-NncPs)] / [[Project page](https://hitachinsk.github.io/publication/2022-10-01-Flow-Guided-Transformer-for-Video-Inpainting)] |
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This repository hosts the pretrained models of the following paper: |
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> **Flow-Guided Transformer for Video Inpainting**<br> |
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> [Kaidong Zhang](https://hitachinsk.github.io/), [Jingjing Fu](https://www.microsoft.com/en-us/research/people/jifu/) and [Dong Liu](http://staff.ustc.edu.cn/~dongeliu/)<br> |
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> European Conference on Computer Vision (**ECCV**), 2022<br> |
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## Details |
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There are three models in this repository, here are the details. |
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- `lafc.pth.tar`: The pretrained model of "Local Aggregation Flow Completion Network", which accepts a sequence of corrupted optical flows, and outputs the completed flows. |
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- `lafc_single.pth.tar`: The pretrained model of the single flow completion version of "Local Aggregation Flow Completion Network", it accepts **one** corrupted flow, and outputs **one** completed flow. |
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- `fgt.pth.tar`: The pretrained model of "Flow Guided Transformer", which receives a sequence of corrupted frames and completed optical flows, and outputs the completed frames. |
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Besides the pretrained weights, we also provide the configuration files of these pretrained models. |
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- `LAFC_config.yaml`: The configuration file of `lafc.pth.tar` |
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- `LAFC_single_config.yaml`: The configuration file of `lafc_single.pth.tar` |
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- `FGT_config.yaml`: The configuration file of `fgt.pth.tar` |
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## Deployment |
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Download this repository to the base directory of the codes (please download that at the github page), and run "bash deploy.sh" to form the models and the cofiguration files. |
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After the step above, you can skip the step 1~3 in the `quick start` section in the github page and run the object removal demo directly. |
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