Bringing Old Films Back to Life

Project Page | Paper (ArXiv) | Supplemental Material

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 Liao^1
1City University of Hong Kong, ²Microsoft Research, ³Microsoft Cloud AI

🌟 Pipeline

Requirements

Please install the dependencies according to environment.yml.

Usage

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.

Train

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.

Test

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.

📔 Citation

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}
}

💡 Acknowledgments

We would like to thank anonymous reviewers for their constructive comments.

📨 Contact

This repo is currently maintained by Ziyu Wan (@Raywzy) and is for academic research use only. Discussions and questions are welcome via [email protected].