# Deep-Video-Inpainting

**Repository Path**: jonbakerfish/Deep-Video-Inpainting

## Basic Information

- **Project Name**: Deep-Video-Inpainting
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-01-26
- **Last Updated**: 2021-01-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep_Video_Inpainting
Official pytorch implementation for "Deep Video Inpainting" (CVPR 2019, TPAMI 2020)  
[Dahun Kim*](https://mcahny.github.io/), [Sanghyun Woo*](https://sites.google.com/view/sanghyunwoo/), [Joon-Young Lee](https://joonyoung-cv.github.io/), and [In So Kweon](https://rcv.kaist.ac.kr). (*: equal contribution)  
[[Paper](https://arxiv.org/abs/1905.01639)] [[Project page](https://sites.google.com/view/deepvinet/)] [[Video results](https://youtu.be/RtThGNTvkjY)]  

If you are also interested in **video caption removal**, please check [[Paper](https://arxiv.org/abs/1905.02949)] [[Project page](https://sites.google.com/view/bvdnet/)] 

## Update
This is recently tested under <b>Python=3.7, Pytorch=1.4, Cudatoolkit=10.0</b>.

<img src="./image/swing4.jpg" width="700" height="350"> 
<img src="./image/roller4.jpg" width="700" height="350"> 

## Disclaimer
This is tested under 
<s>Python=3.6, PyTorch=0.4.0</s>  
Python=3.7, Pytorch=1.4, Cudatoolkit=10.0.

## Testing
0. Download the trained weight 'save_agg_rec_512.pth' and place it in "./results/vinet_agg_rec/"  
Google drive: [[weight-512x512](https://drive.google.com/file/d/1KAi9oQVBaJU9ytr7dYr2WwEcO5NLiJvo/view?usp=sharing)] [[weight-256x256](https://drive.google.com/file/d/1UCDZVJbymiHUcD2GfLq9-NU65MW6rSY4/view?usp=sharing)]   

1. Create conda env and install Pytorch library.
```
conda create -n vinet python=3.7 -y
conda activate vinet
conda install pytorch=1.4 torchvision cudatoolkit=10.0 -c pytorch -y
```

2. Compile Resample2d, Correlation dependencies.  
```python
bash ./install.sh
```

3. Run the demo (the results are saved in "./results/vinet_agg_rec/davis_512/").  
```python
python demo_vi.py
```

4. Optional: Run the video retargeting (Section 4.5)
```python
python demo_retarget.py
```
<img src="./image/bmx-trees.png" width="700" height="140"> 


## Citation
If you find the codes useful in your research, please cite:  
```bibtex
@inproceedings{kim2019deep,
  title={Deep Video Inpainting},
  author={Kim, Dahun and Woo, Sanghyun and Lee, Joon-Young and Kweon, In So},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5792--5801},
  year={2019},}
```
```bibtex
@ARTICLE{kim2020vipami,
  author={Kim, Dahun and Woo, Sanghyun and Lee, Joon-Young and Kweon, In So},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  title={Recurrent Temporal Aggregation Framework for Deep Video Inpainting},
  year={2020},
  volume={42},
  number={5},
  pages={1038-1052},}
```