lifeisstrange / derendering-text Goto Github PK

Wataru Shimoda¹, Daichi Haraguchi², Seiichi Uchida², Kota Yamaguchi^1
1CyberAgent.Inc, ² Kyushu University
Accepted to ICCV2021. [Publication] [Arxiv] [project-page]

This repository contains the codes for "De-rendering stylized texts".

We propose to parse rendering parameters of stylized texts utilizing a neural net.

The proposed model parses rendering parameters based on famous 2d graphic engine[Skia.org|python implementation], which has compatibility with CSS in the Web. We can export the estimated rendering parameters and edit texts by an off-the-shelf rendering engine.

• Python >= 3.7
• Pytorch >= 1.8.1
• torchvision >= 0.9.1

pip install -r requiements.txt

• The proposed model is trained with google fonts.
• Download google fonts and locate in data/fonts/ as gfonts.

cd data/fonts
git clone https://github.com/google/fonts.git gfonts

• The proposed model parses rendering parameters and refines them through the differentiable rendering model, which uses pre-rendered alpha maps.
• Generate pre-rendered alpha maps.

python -m util_lib.gen_pams

Pre-rendered alpha maps would be generated in data/fonts/prerendered_alpha.

• Download the pre-trained weight from this link (weight).
• Locate the weight file in weights/font100_unified.pth.

Example usage.

python test.py --imgfile=example/sample.jpg

Note

• imgfile option: path of an input image
• results would be generated in res/

in progress

in progress

• Testing codes
• Codes for the text image generator
• Training codes
• Add notebooks for the guide

@InProceedings{Shimoda_2021_ICCV,
    author    = {Shimoda, Wataru and Haraguchi, Daichi and Uchida, Seiichi and Yamaguchi, Kota},
    title     = {De-Rendering Stylized Texts},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1076-1085}
}

This repository is maintained by Wataru shimoda(wataru_shimoda[at]cyberagent.co.jp).