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AGIS-Net

Introduction

This is the official PyTorch implementation of the Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning.

paper | supplementary material

Abstract

Automatic generation of artistic glyph images is a challenging task that attracts many research interests. Previous methods either are specifically designed for shape synthesis or focus on texture transfer. In this paper, we propose a novel model, AGIS-Net, to transfer both shape and texture styles in one-stage with only a few stylized samples. To achieve this goal, we first disentangle the representations for content and style by using two encoders, ensuring the multi-content and multi-style generation. Then we utilize two collaboratively working decoders to generate the glyph shape image and its texture image simultaneously. In addition, we introduce a local texture refinement loss to further improve the quality of the synthesized textures. In this manner, our one-stage model is much more efficient and effective than other multi-stage stacked methods. We also propose a large-scale dataset with Chinese glyph images in various shape and texture styles, rendered from 35 professional-designed artistic fonts with 7,326 characters and 2,460 synthetic artistic fonts with 639 characters, to validate the effectiveness and extendability of our method. Extensive experiments on both English and Chinese artistic glyph image datasets demonstrate the superiority of our model in generating high-quality stylized glyph images against other state-of-the-art methods.

Model Architecture

Architecture

Skip ConnectionLocal Discriminator
skip-connectionlocal-discriminator

Some Results

comparison

comparison

across_languae

Prerequisites

Get Started

Installation

  1. Install PyTorch, torchvison and dependencies from https://pytorch.org
  2. Install python libraries visdom and dominate:
    pip install visdom
    pip install dominate
    
  3. Clone this repo:
    git clone -b master --single-branch https://github.com/hologerry/AGIS-Net
    cd AGIS-Net
    
  4. Download the offical pre-trained vgg19 model: vgg19-dcbb9e9d.pth, and put it under the models/ folder

Datasets

The datasets server is down, you can download the datasets from PKU Disk, Dropbox or MEGA. Download the datasets using the following script, four datasets and the raw average font style glyph image are available.

It may take a while, please be patient

bash ./datasets/download_dataset.sh DATASET_NAME

Please refer to the data for more details about our datasets and how to prepare your own datasets.

Model Training

Model Testing

Acknowledgements

This code is inspired by the BicycleGAN.

Special thanks to the following works for sharing their code and dataset.

Citation

If you find our work is helpful, please cite our paper:

@article{Gao2019Artistic,
  author = {Gao, Yue and Guo, Yuan and Lian, Zhouhui and Tang, Yingmin and Xiao, Jianguo},
  title = {Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning},
  journal = {ACM Trans. Graph.},
  issue_date = {November 2019},
  volume = {38},
  number = {6},
  year = {2019},
  articleno = {185},
  numpages = {12},
  url = {http://doi.acm.org/10.1145/3355089.3356574},
  publisher = {ACM}
} 

Copyright

The code and dataset are only allowed for PERSONAL and ACADEMIC usage.