Unpaired Portrait Drawing Jittor Implementation

We provide Jittor implementations for our CVPR 2020 paper "Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping". paper, suppl.

This project generates multi-style 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

From left to right: input, output(style1), output(style2), output(style3)

Installation

• To install the dependencies, run

pip install -r requirements.txt

Apply pretrained model

• 1. Download pre-trained models from BaiduYun(extract code:c9h7) or GoogleDrive and rename the folder to checkpoints.
• 2. Test for example photos: generate artistic portrait drawings for example photos in the folder ./samples using

python test.py --input_folder ./samples

Results are saved in ./results/portrait_drawing/pretrained_200

• 3. To test on your own photos: First use an image editor to crop the face region of your photo (or use an optional preprocess here). Then specify the folder that contains test photos using option --input_folder, and run the test.py again.

Train

• 1. Prepare for the dataset: 1) download face photos and portrait drawings from internet (e.g. resources). 2) align, crop photos and drawings & 3) prepare nose, eyes, lips masks according to preprocess instructions. 3) put aligned photos under ./data/portrait_drawing/train/A, aligned drawings under ./data/portrait_drawing/train/B, masks under A_nose,A_eyes,A_lips,B_nose,B_eyes,B_lips respectively.
• 2. Train a 3-class style classifier and extract the 3-dim style feature (according to paper). And save the style feature of each drawing in the training set in .npy format, in folder ./data/portrait_drawing/train/B_feat

A subset of our training set is here.

• 3. Train our model (200 epochs)

python asymmetric_cyclegan.py

Models are saved in folder checkpoints/portrait_drawing

• 4. Test the trained model

python test.py --which_epoch 200 --model_name portrait_drawing

Results are saved in ./results/portrait_drawing/portrait_drawing_200

Citation

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

@inproceedings{YiLLR20,
  title     = {Unpaired Portrait Drawing Generation via Asymmetric Cycle Mapping},
  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 '20)},
  pages     = {8214--8222},
  year      = {2020}
}