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Few-shot Compositional Font Generation with Dual Memory (ECCV'20)

NOTICE: We release the unified few-shot font generation repository (clovaai/fewshot-font-generation). If you are interested in using our implementation, please visit the unified repository.

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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.

Abstract

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.

Other related repositories

You can find more related projects on the few-shot font generation at the following links:

Installation

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.

Dataset preparation

Data sources

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.

Dumping dataset

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.

python -m scripts.prepare_dataset kor $FONTSDIR meta/kor_split.json $DUMPDIR

python -m scripts.prepare_dataset thai $FONTSDIR meta/thai_split.json $DUMPDIR

How to run

Pretrained models

For convenience, the minimal size checkpoints are provided by excluding training variables, e.g., momentums in optimizer, discriminator, and non-EMA generator.

Training

python train.py $NAME cfgs/kor.yaml

python train.py $NAME cfgs/kor.yaml cfgs/thai.yaml

Generation & Pixel-level evaluation

python evaluator.py $NAME $CHECKPOINT_PATH $OUT_DIR cfgs/kor.yaml --mode cv-save

python evaluator.py $NAME $CHECKPOINT_PATH $OUT_DIR cfgs/kor.yaml cfgs/thai.yaml --mode cv-save

python evaluator.py $NAME $CHECKPOINT_PATH $OUT_DIR cfgs/kor.yaml --mode user-study-save

Citation

@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)},
}

License

This project is distributed under MIT license, except modules.py which is adopted from https://github.com/NVlabs/FUNIT.

Copyright (c) 2020-present NAVER Corp.

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