๐Paper | ๐Project Page | ๐Anime Scene Dataset | ๐คDemo
- [08/2023] Integrated to Hugging Face. Enjoy the web demo!
- [08/2023] Inference code and Dataset is released.
- [08/2023] Project page is built.
- [07/2023] The paper is accepted to ICCV 2023!
- Clone this repo:
git clone https://github.com/Yuxinn-J/Scenimefy.git cd Scenimefy
- Install dependent packages:
after installing Anaconda, you can create a new Conda environment using
conda env create -f Semi_translation/environment.yml
.
-
Download pre-trained models: Shinkai_net_G.pth
wget https://github.com/Yuxinn-J/Scenimefy/releases/download/v0.1.0/Shinkai_net_G.pth -P Semi_translation/pretrained_models/shinkai-test/
-
Inference! Simply run the following command, or refer the
./Semi_translation/script/test.sh
for detailed usage:cd Semi_translation python test.py --name shinkai-test --CUT_mode CUT --model cut --phase test --epoch Shinkai --preprocess none
- Results are in the
./Semi_translation/results/shinkai-test/
by default. - To prepare your own test images, you can refer to the data folder structure in
./Semi_translation/datasets/Sample
, and place your test images intestA
.
- Results are in the
- We provide a UI for testing Scenimefy, which is built with gradio. To launch the demo, simply execute the following command in your terminal:
git clone https://huggingface.co/spaces/YuxinJ/Scenimefy pip install -r requirements.txt pip install gradio python app.py
- This demo is also hosted on Hugging Face๐ค.
It is a high-quality anime scene dataset comprising 5,958 images with the following features:
- High-resolution (1080ร1080)
- Shinkai-style (from 9 Mokota Shinkai films)
- Pure anime scene: manual dataset curation by eliminating irrelevant and low-quality images
In compliance with copyright regulations, we cannot directly release the anime images. However, you can conveniently prepare the dataset following instructions here.
If you find this work useful for your research, please consider citing our paper:
@inproceedings{jiang2023scenimefy,
title={Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation},
author={Jiang, Yuxin and Jiang, Liming and Yang, Shuai and Loy, Chen Change},
booktitle={ICCV},
year={2023}
}
Our code is mainly developed based on Cartoon-StyleGAN and Hneg_SRC. We thank facebook for their contribution of Mask2Former.
Distributed under the S-Lab License. See LICENSE.md for more information.