Official PyTorch implementation of DM-Font | Paper | Video on ECCV 2020
Junbum Cha, Sanghyuk Chun, Gayoung Lee, Bado Lee, Seonghyeon Kim, Hwalsuk Lee.
Clova AI Research, NAVER Corp.
In ECCV 2020.
Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Despite the remarkable success of existing font generation methods, they have significant drawbacks; they require a large number of reference images to generate a new font set, or they fail to capture detailed styles with a few samples. In this paper, we focus on compositional scripts, a widely used letter system in the world, where each glyph can be decomposed by several components. By utilizing the compositionality of compositional scripts, we propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font), which enables us to generate a high-quality font library with only a few samples. We employ memory components and global-context awareness in the generator to take advantage of the compositionality. In the experiments on Korean-handwriting fonts and Thai-printing fonts, we observe that our method generates a significantly better quality of samples with faithful stylization compared to the state-of-the-art generation methods in quantitatively and qualitatively.
You can find more related projects on the few-shot font generation at the following links:
- clovaai/dmfont (ECCV'20) | paper
- clovaai/lffont (AAAI'21) | paper
- clovaai/mxfont (ICCV'21) | paper
Install dependencies:
pip install -r requirements.txt
Note that using different version of required packages can effects the results, especially PyTorch. The implementations are tested on Python 3.6.
Korean-handwriting and Thai-printing datasets were built from UhBee fonts and Thai font collection, respectively. To ensure the style diversity of the dataset, one font was selected for each font family in our experiments.
The scripts/prepare_dataset.py script renders glyphs from the collected ttf fonts and dumps them into hdf5 files.
For the Thai-printing dataset, we rectify the rendering errors using raqm.
It should be installed before making dataset.
- Korean-handwriting
python -m scripts.prepare_dataset kor $FONTSDIR meta/kor_split.json $DUMPDIR
- Thai-printing
python -m scripts.prepare_dataset thai $FONTSDIR meta/thai_split.json $DUMPDIR
For convenience, the minimal size checkpoints are provided by excluding training variables, e.g., momentums in optimizer, discriminator, or non-EMA generator.
- Checkpoint trained on the Korean-handwriting dataset (Google drive)
- Checkpoint trained on the Thai-printing dataset (Google drive)
- Korean-handwriting
python train.py $NAME cfgs/kor.yaml
- Thai-printing
python train.py $NAME cfgs/kor.yaml cfgs/thai.yaml
- Korean-handwriting
python evaluator.py $NAME $CHECKPOINT_PATH $OUT_DIR cfgs/kor.yaml --mode cv-save
- Thai-printing
python evaluator.py $NAME $CHECKPOINT_PATH $OUT_DIR cfgs/kor.yaml cfgs/thai.yaml --mode cv-save
- Korean-unrefined
python evaluator.py $NAME $CHECKPOINT_PATH $OUT_DIR cfgs/kor.yaml --mode user-study-save
@inproceedings{cha2020dmfont,
title={Few-shot Compositional Font Generation with Dual Memory},
author={Cha, Junbum and Chun, Sanghyuk and Lee, Gayoung and Lee, Bado and Kim, Seonghyeon and Lee, Hwalsuk},
year={2020},
booktitle={European Conference on Computer Vision (ECCV)},
}
This project is distributed under MIT license, except modules.py which is adopted from https://github.com/NVlabs/FUNIT.
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