Lagent_003_concat.mp4
This repository is the official pytorch implementation of our CVPR 2022 paper, Bringing Old Films Back to Life.
Ziyu Wan1,
Bo Zhang2,
Dongdong Chen3,
Jing Liao1
1City University of Hong Kong, 2Microsoft Research, 3Microsoft Cloud AI
Please install the dependencies according to environment.yml
.
Clone the repository
git clone https://github.com/raywzy/Bringing-Old-Films-Back-to-Life.git
Download the relabled scratch templates for the video degradation model. Download
REDS dataset could be directly downloaded from Link.
Create a folder ./pretrained_models
mkdir pretrained_models
Put the optical flow estimation model weights raft-sintel.pth
in ./pretrained_models
, which could be downloaded here.
To train a model, remember to modify the config file following the example config_example/config.yaml
.
NOTE: Modify both "train.dataroot_gt" and "train.dataroot_lq" into the path of clean training frame since the degradation is generated on-the-fly.
Modify "val.dataroot_gt" and "val.dataroot_lq" to the path of validation video clips.
Set "texture_template" to the path where you download the scratch templates.
Then you could run
CUDA_VISIBLE_DEVICES=0 python VP_code/main_gan.py --name RNN_Swin_4 --model_name RNN_Swin_4 --epoch 20 --nodes 1 --gpus 1 --discriminator_name discriminator_v2 --which_gan hinge
You could enable "--fix_flow_estimator" which freezes the flow-estimation network to make the training more stable.
We provide the pre-trained models and some testing old films in ./test_data
.
If you'd like to directly use the provided model weights, please create a folder ./OUTPUT
:
mkdir OUTPUT
Put RNN_Swin_4.zip
in the ./OUTPUT
folder, then unzip it by
unzip RNN_Swin_4.zip
To restore the old films, please run
CUDA_VISIBLE_DEVICES=0 python VP_code/test.py --name RNN_Swin_4 --model_name RNN_Swin_4 \
--which_iter 200000 --temporal_length 20 --temporal_stride 10 \
--input_video_url your_path/./test_data \
--gt_video_url your_path/./test_data
The restored results could be found in ./OUTPUT
folder.
Note: Currently the model is only trained on REDS dataset then the learned texture information will be limited, to obtain better generalization performance please consider training the model on more diverse videos.
If you find our work useful for your research, please consider citing the following papers :)
@article{wan2022oldfilm,
title={Bringing Old Films Back to Life},
author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Liao, Jing},
journal={CVPR},
year={2022}
}
Want to restore the old photos as well? Try and cite our old photo restoration algorithm here.
@inproceedings{wan2020bringing,
title={Bringing Old Photos Back to Life},
author={Wan, Ziyu and Zhang, Bo and Chen, Dongdong and Zhang, Pan and Chen, Dong and Liao, Jing and Wen, Fang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2747--2757},
year={2020}
}
Powerful Image Completion Transformer (ICT), which could effectively recover the masked regions.
@article{wan2021high,
title={High-Fidelity Pluralistic Image Completion with Transformers},
author={Wan, Ziyu and Zhang, Jingbo and Chen, Dongdong and Liao, Jing},
journal={arXiv preprint arXiv:2103.14031},
year={2021}
}
We would like to thank anonymous reviewers for their constructive comments.
This repo is currently maintained by Ziyu Wan (@Raywzy) and is for academic research use only. Discussions and questions are welcome via raywzy@gmail.com.