MovingColor: Seamless Fusion of Fine-grained Video Color Enhancement

Official PyTorch Implementation of ACM MM 2024 (Oral) Paper "MovingColor: Seamless Fusion of Fine-grained Video Color Enhancement"

Environment

Tested on Ubuntu 20.04 + CUDA 11.3

conda create -n movingcolor python=3.9 -y
conda activate movingcolor
conda install -c conda-forge ffmpeg
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install -r requirements.txt

Data Preparation

Training

1. Download YouTube-VOS dataset and put it in datasets/youtube-vos.
2. Unzip frames and resize to datasets/youtube-vos/train_all_frames/JPEGImages_resize/output_432x240

Train

python train.py --config configs/train_MCGenerator_l1_vgg_gan.json

Inference

python inference.py --config configs/inference_MCGenerator_l1_vgg_gan.json

Acknowledgments

This code repository is partially borrowed from LaMa and ProPainter.

Citation

You can cite it as follows:

@inproceedings{dong2024movingcolor,
author = {Dong, Yi and Wang, Yuxi and Fang, Zheng and Ouyang, Wenqi and Lin, Xianhui and Shen, Zhiqi and Ren, Peiran and Xie, Xuansong and Huang, Qingming},
title = {MovingColor: Seamless Fusion of Fine-grained Video Color Enhancement},
year = {2024},
isbn = {9798400706868},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3664647.3681130},
doi = {10.1145/3664647.3681130},
booktitle = {Proceedings of the 32nd ACM International Conference on Multimedia},
pages = {7454–7463},
numpages = {10},
keywords = {color fusion, video color enhancement, video editing},
location = {Melbourne VIC, Australia},
series = {MM '24}
}