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APDrawingGAN Jittor Implementation

We provide Jittor implementations for our CVPR 2019 oral paper "APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs". [Paper]

This project generates artistic portrait drawings from face photos using a GAN-based model.

Prerequisites

• Linux or macOS
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Sample Results

Up: input, Down: output

Installation

• To install the dependencies, run

pip install -r requirements.txt

Apply pretrained model

• 1. Download pre-trained models from BaiduYun(extract code: 9qhp) and rename the folder to checkpoints.
• 2. Test for example photos: generate artistic portrait drawings for example photos in the folder ./samples/A/example using models in checkpoints/formal_author

python test.py

Results are saved in ./results/portrait_drawing/formal_author_300/example

• 3. To test on your own photos: First run preprocess here). Then specify the folder that contains test photos using option --input_folder, specify the folder of landmarks using --lm_folder and the folder of masks using --mask_folder, and run the test.py again.

Train

• 1. Download the APDrawing dataset from GoogleDrive and put the folder to data/apdrawing.
• 2. Train our model (300 epochs)

python apdrawing_gan.py

Models are saved in folder checkpoints/apdrawing

• 4. Test the trained model

python test.py --which_epoch 300 --model_name apdrawing

Results are saved in ./results/portrait_drawing/apdrawing_300/example

Citation

If you use this code or APDrawing dataset for your research, please cite our paper.

@inproceedings{YiLLR19,
  title     = {{APDrawingGAN}: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs},
  author    = {Yi, Ran and Liu, Yong-Jin and Lai, Yu-Kun and Rosin, Paul L},
  booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition (CVPR '19)},
  pages     = {10743--10752},
  year      = {2019}
}