ColorMNet: A Memory-based Deep Spatial-Temporal Feature Propagation Network for Video Colorization

Project Page | Paper (ArXiv) | Supplemental Material | Code (Github)

This repository is the official pytorch implementation of our paper, ColorMNet: A Memory-based Deep Spatial-Temporal Feature Propagation Network for Video Colorization.

Yixin Yang, Jiangxin Dong, Jinhui Tang, Jinshan Pan

Nanjing University of Science and Technology

🔥 News

• [2024-09-09] Add training code, see train.py.
• [2024-09-09] Colab demo for ColorMNet is available at .
• [2024-09-07] Add inference code and pretrained weights, see test.py.
• [2024-04-13] Project page released at ColorMNet Project. Please be patient and stay updated.

Requirements

• Python 3.8+
• PyTorch 1.11+ (See PyTorch for installation instructions)
• torchvision corresponding to the PyTorch version
• OpenCV (try pip install opencv-python)
• Others: pip install -r requirements.txt

💼 Dependencies and Installation

# git clone this repository

conda create -n colormnet python=3.8 
conda activate colormnet 

pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118

# install py-thin-plate-spline
git clone https://github.com/cheind/py-thin-plate-spline.git
cd py-thin-plate-spline && pip install -e . && cd ..

# install Pytorch-Correlation-extension
git clone https://github.com/ClementPinard/Pytorch-Correlation-extension.git 
cd Pytorch-Correlation-extension && python setup.py install && cd ..

pip install -r requirements.txt

🎁 Checkpoints

Download the pretrained models manually and put them in ./saves (create the folder if it doesn't exist).

Name	URL
ColorMNet	model

⚡ Quick Inference

• Test on Images:

  For Windows users, please follow RuntimeError to avoid multiprocessor Runtime error in data loader. Thanks to @UPstud.

CUDA_VISIBLE_DEVICES=0 python test.py 
# Add --FirstFrameIsNotExemplar if the reference frame is not exactly the first input image. Please make sure the ref frame and the input frames are of the same size. 

Train

Dataset structure for both the training set and the validation set

# Specify --davis_root and --validation_root
data_root/
├── 001/
│   ├── 00000.png
│   ├── 00001.png
│   ├── 00002.png
│   └── ...
├── 002/
│   ├── 00000.png
│   ├── 00001.png
│   ├── 00002.png
│   └── ...
└── ...

Training script

CUDA_VISIBLE_DEVICES=0 python -m torch.distributed.run \
    --master_port 25205 \
    --nproc_per_node=1 \
    train.py \
    --exp_id DINOv2FeatureV6_LocalAtten_DAVISVidevo \
    --davis_root /path/to/your/training/data/\
    --validation_root /path/to/your/validation/data\
    --savepath ./wandb_save_dir

To Do

• Release training code
• Release testing code
• Release pre-trained models
• Release demo

Citation

If our work is useful for your research, please consider citing:

@inproceedings{yang2024colormnet,
    author = {Yang, Yixin and Dong, Jiangxin and Tang, Jinhui and Pan Jinshan},
    title = {ColorMNet: A Memory-based Deep Spatial-Temporal Feature Propagation Network for Video Colorization},
    booktitle = {ECCV},
    year = {2024}
}

License

This project is licensed under BY-NC-SA 4.0, while some methods adopted in this project are with other licenses. Please refer to LICENSES.md for the careful check. Redistribution and use should follow this license.

Acknowledgement

This project is based on XMem. Some codes are brought from DINOv2. Thanks for their awesome works.

Contact

This repo is currently maintained by Yixin Yang (@yyang181) and is for academic research use only.