68 lines
1.8 KiB
Markdown
68 lines
1.8 KiB
Markdown
# Deep Exemplar-based Video Colorization (Pytorch Implementation)
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<img src='assets/teaser.png'/>
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### [Paper](https://arxiv.org/abs/1906.09909) | [Pretrained Model]() | [Youtube video](https://youtu.be/HXWR5h5vVYI)
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**Deep Exemplar-based Video Colorization, CVPR2019**
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[Bo Zhang](https://www.microsoft.com/en-us/research/people/zhanbo/)<sup>1,3</sup>,
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[Mingming He](http://mingminghe.com/)<sup>1,5</sup>,
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[Jing Liao](https://liaojing.github.io/html/)<sup>2</sup>,
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[Pedro V. Sander](https://www.cse.ust.hk/~psander/)<sup>1</sup>,
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[Lu Yuan](https://www.microsoft.com/en-us/research/people/luyuan/)<sup>4</sup>,
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[Amine Bermak](https://eebermak.home.ece.ust.hk/)<sup>1</sup>,
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[Dong Chen](https://www.microsoft.com/en-us/research/people/doch/)<sup>3</sup> <br>
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<sup>1</sup>Hong Kong University of Science and Technology,<sup>2</sup>City University of Hong Kong,
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<sup>3</sup>Microsoft Research Asia, <sup>4</sup>Microsoft Cloud&AI, <sup>5</sup>USC Institute for Creative Technologies
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## Prerequisites
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- Python 3.6+
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- Nvidia GPU + CUDA, CuDNN
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## Installation
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```bash
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conda create -n ColorVid python=3.6
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source activate ColorVid
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pip install -r requirements.txt
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```
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## Data preparation
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checkpoint, pre-trained vgg
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- input video frames
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- reference image
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- output
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## Test
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```bash
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python test.py
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```
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## TODO
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- [ ] Release the training code
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- [ ] Prepare the Colab Demo
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- [ ] Output the video
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- [ ] GIF teaser
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## Citation
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If you use this code for your research, please cite our paper.
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> @inproceedings{zhang2019deep,
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> title={Deep exemplar-based video colorization},
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> author={Zhang, Bo and He, Mingming and Liao, Jing and Sander, Pedro V and Yuan, Lu and Bermak, Amine and Chen, Dong},
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> booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
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> pages={8052--8061},
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> year={2019}
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> }
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## License
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This project is licensed under the MIT license.
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