This is the official Pytorch implementation of the paper:
Yizhi Wang and Zhouhui Lian. DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning. SIGGRAPH Asia. 2021.
Paper: arxiv Homepage: DeepVecFont
Given a few vector glyphs of a font as reference, our model generates the full vector font:
Input glyphs:
Input glyphs:
Input glyphs:
Rendered:
Rendered:
- python 3.9
- Pytorch 1.9 (it may work on some lower versions, but not tested)
Please use Anaconda to build the environment:
conda create -n dvf python=3.9
source activate dvfInstall pytorch via the instructions.
We utilize diffvg to refine our generated vector glyphs in the testing phase. Please go to https://github.com/BachiLi/diffvg see how to install it.
Important (updated 2021.10.19): You need first replace the original diffvg/pydiffvg/save_svg.py with this and then install.
- The Vector Font dataset Download links: Google Drive
Please download the vecfont_dataset dir and put it under ./data/.
(This dataset is a subset from SVG-VAE, ICCV 2019.
The details about how to create from your own data are shown below.)
- The Image Super-resolution dataset
This dataset is too huge so I suggest to create it by yourself (see the details below about creating your own dataset).
- The mean and stdev files Download links: Google Drive
Please Download them and put it under ./data/.
- The Neural Rasterizer Download links: Google Drive
Please download the dvf_neural_raster dir and put it under ./experiments/.
- The Image Super-resolution model Download links: Google Drive.
Please download the image_sr dir and put it under ./experiments/.
- The Main model Download links: Google Drive.
Please download the dvf_main_model dir and put it under ./experiments/.
Note that recently we switched from Tensorflow to Pytorch, we may update the models that have better performances.
To train our main model, run
python main.py --mode train --experiment_name dvf --model_name main_model
The configurations can be found in options.py.
To test our main model, run
python test_sf.py --mode test --experiment_name dvf --model_name main_model --test_epoch 1500 --batch_size 1 --mix_temperature 0.0001 --gauss_temperature 0.01
This will output the synthesized fonts without refinements. Note that batch_size must be set to 1. The results will be written in ./experiments/dvf_main_model/results/.
To refinement the vector glyphs, run
python refinement.mp.py --experiment_name dvf --fontid 14 --candidate_nums 20
where the fontid denotes the index of testing font. The results will be written in ./experiments/dvf_main_model/results/0014/svgs_refined/. Set num_processes according to your GPU's computation capacity.
We have pretrained the neural rasterizer and image super-resolution model. If you want to train them yourself:
To train the neural rasterizer:
python train_nr.py --mode train --experiment_name dvf --model_name neural_raster
To train the image super-resolution model:
python train_sr.py --mode train --name image_sr
- Prepare ttf/otf files
Put the ttf/otf files in ./data_utils/font_ttfs/train and ./data_utils/font_ttfs/test, and organize them as 0000.ttf, 0001.ttf, 0002.ttf...
- Deactivate the conda environment and install Fontforge
for python > 3.0:
conda deactivate
apt install python3-fontforge
- Get SFD files via Fontforge
cd data_utils
python convert_ttf_to_sfd_mp.py --split train
python convert_ttf_to_sfd_mp.py --split test
- Generate glyph images
python write_glyph_imgs.py --split train
python write_glyph_imgs.py --split test
- package them to pkl files
python write_data_to_pkl.py --split train
python write_data_to_pkl.py --split test
Note: we package the image and sequence files all into the pkls, which is memory-consuming when training. It is a better way to write the file paths and read the files from disks/drives when training (TO-DO).
If you use this code or find our work is helpful, please consider citing our work:
@article{wang2021deepvecfont,
author = {Wang, Yizhi and Lian, Zhouhui},
title = {DeepVecFont: Synthesizing High-quality Vector Fonts via Dual-modality Learning},
journal = {ACM Transactions on Graphics},
numpages = {15},
volume={40},
number={6},
month = December,
year = {2021},
doi={10.1145/3478513.3480488}
}