Anime2Sketch: A sketch extractor for illustration, anime art, manga
By Xiaoyu Xiang
- 2021.5.24: Fix an interpolation error and a GPU inference error.
- 2021.5.12: Web Demo by AK391
- 2021.5.2: Upload more example results of anime video.
- 2021.4.30: Upload the test scripts. Now our repo is ready to run!
- 2021.4.11: Upload the pretrained weights, and more test results.
- 2021.4.8: Create the repo.
The repository contains the testing codes and pretrained weights for Anime2Sketch.
Anime2Sketch is a sketch extractor that works well on illustration, anime art, and manga. It is an application based on the paper "Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis".
Install the required packages: pip install -r requirements.txt
Please download the weights from GoogleDrive, and put it into the weights/ folder.
python3 test.py --dataroot /your_input/dir --load_size 512 --output_dir /your_output/dirThe above command includes three arguments:
- dataroot: your test file or directory
- load_size: due to the memory limit, we need to resize the input image before processing. By default, we resize it to
512x512. - output_dir: path of the output directory
Run our example:
python3 test.py --dataroot test_samples/madoka.jpg --load_size 512 --output_dir results/This project is a sub-branch of AODA. Please check it for the training instructions.
Our model works well on illustration arts:
Turn handrawn photos to clean linearts:
Simplify freehand sketches:
And more anime results:
You can also leave your questions as issues in the repository. I will be glad to answer them!
This project is released under the MIT License.
@misc{Anime2Sketch,
author = {Xiaoyu Xiang, Ding Liu, Xiao Yang, Yiheng Zhu, Xiaohui Shen},
title = {Anime2Sketch: A Sketch Extractor for Anime Arts with Deep Networks},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/Mukosame/Anime2Sketch}}
}
@misc{xiang2021adversarial,
title={Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis},
author={Xiang, Xiaoyu and Liu, Ding and Yang, Xiao and Zhu, Yiheng and Shen, Xiaohui and Allebach, Jan P},
year={2021},
eprint={2104.05703},
archivePrefix={arXiv},
primaryClass={cs.CV}
}