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Official pytorch implementation for "Deep Video Inpainting" (CVPR 2019)
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DAVIS_demo test codes Jun 30, 2019
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lib .gitignore Jul 6, 2019
models correlation package Jul 6, 2019
results/vinet_agg_rec .gitignore Jul 6, 2019
.gitignore .gitignore Jul 6, 2019
README.md readme Jul 10, 2019
davis.py test codes Jun 30, 2019
demo_retarget.py vid retrageting added Jul 10, 2019
demo_vi.py pre:30 Jul 6, 2019
install.sh install.sh Jul 6, 2019
model.py vinet final Jun 30, 2019
utils.py test codes Jun 30, 2019

README.md

Deep_Video_Inpainting

Official pytorch implementation for "Deep Video Inpainting" (CVPR 2019)
Dahun Kim*, Sanghyun Woo*, Joon-Young Lee, and In So Kweon. (*: equal contribution)
[Paper] [Project page] [Video results]

If you are also interested in video caption removal, please check [Paper] [Project page]

Disclaimer

This is tested under Python 3.6, PyTorch 0.4.0 (dependencies can be compiled on this version).

Testing

  1. Download the trained weight 'save_agg_rec_512.pth' and place it in "./results/vinet_agg_rec/"
    Google drive: [weight-512x512] [weight-256x256]

  2. Compile Resample2d, Correlation dependencies.

bash ./install.sh
  1. Run the demo (the results are saved in "./results/vinet_agg_rec/davis_512/").
python demo_vi.py
  1. Optional: Run the video retargeting (Section 4.5)
python demo_retarget.py

Citation

If you find the codes useful in your research, please cite:

@inproceedings{kim2019deep,
  title={Deep Video Inpainting},
  author={Kim, Dahun and Woo, Sanghyun and Lee, Joon-Young and So Kweon, In},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5792--5801},
  year={2019}
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