Arbitrary Video Style Transfer via Multi-Channel Correlation
Yingying Deng, Fan Tang, Weiming Dong, Haibin Huang, Chongyang Ma, Changsheng Xu
Results presentation Visual comparisons of video style transfer results. The first row shows the video frame stylized results. The second row shows the heat maps which are used to visualize the differences between two adjacent video frame.
Framework Overall structure of MCCNet.
Experiment
Requirements
- python 3.6
- pytorch 1.4.0
- PIL, numpy, scipy
- tqdm
Testing
Pretrained models: vgg-model, [decoder], [MCC_module](see above)
Please download them and put them into the floder ./experiments/
python test_video.py --content_dir input/content/ --style_dir input/style/ --output out
Training
Traing set is WikiArt collected from WIKIART
Testing set is COCO2014
python train.py --style_dir ../../datasets/Images --content_dir ../../datasets/train2014 --save_dir models/ --batch_size 4
Reference
If you use our work in your research, please cite us using the following BibTeX entry ~ Thank you ^ . ^. Paper Link [pdf](coming soon)
@inproceedings{deng:2020:arbitrary,
title={Arbitrary Video Style Transfer via Multi-Channel Correlation},
author={Deng, Yingying and Tang, Fan and Dong, Weiming and Huang, haibin and Ma chongyang and Xu, Changsheng},
booktitle={AAAI},
year={2021},
}