cv-ip/artediting

Modeling Artistic Workflows for Image Generation and Editing (ECCV 2020)

Modeling Artistic Workflows for Image Generation and Editing

[Project Page][Paper]

Pytorch implementation for our artwork generation and editing method. The proposed design can 1) model creation workflows for different types of artwork and 2) enable both the image generation and editing at different workflow stages.

Paper

Please cite our paper if you find the code or dataset useful for your research.

Modeling Artistic Workflows for Image Generation and Editing
Hung-Yu Tseng, Matt Fisher, Jingwan (Cynthia) Lu, Yijun Li, Vladimir (Vova) Kim, Ming-Hsuan Yang
European Conference on Computer Vision (ECCV), 2020

@inproceedings{tseng2020artediting,
  author = {Tseng, Hung-Yu and Fisher, Matthew and Lu, Jingwan and Li, Yijun and Kim, Vladimir and Yang, Ming-Hsuan},
  booktitle = {European Conference on Computer Vision},
  title = {Modeling Artistic Workflows for Image Generation and Editing},
  year = {2020}
}

Usage

Installation

Clone this repo:

git clone https://github.com/hytseng0509/ArtEditing
cd ArtEditing

Install packages:

conda create --name artediting python=3.6
conda activate artediting
conda install pytorch torchvision -c pytorch
pip install -r requirements.txt

Datasets

Download the face or anime datasets using the following script:

• Set DATASET to face or anime

cd data
python download_dataset.sh DATASET
cd ..

Workflow Inference Training

We first train the workflow inference model.

python train_inference.py  --name face_inference --n_ep_separate 15 --n_ep_joint 15

Artwork Generation Training

Then we load the trained inference model and train the artwork generation model. We need 4 GPUs for batch size of 8.

python train_generation.py --gpu_ids 0,1,2,3 --name face --n_ep_separate 40 --n_ep_joint 15 --load_inference face_inference/30.pth

Learning-Based Regularization Training

Finally, for each workflow stage, we train the regularization for the input image reconstruction.

python train_regularization.py --load face/55.pth --name face_reg0 --reg_stage 0
python train_regularization.py --load face/55.pth --name face_reg1 --reg_stage 1

Testing

Generate reconsturction and random editing results:

python test.py --name face_results --load face/55.pth --reg_load face_reg0/500.pth,face_reg1/500.pth

The results can be found at results/face_results.

Notes

• Part of this implementation is modified from BicycleGAN and MUNIT.
• The dataset, model, and code are for non-commercial research purposes only.