Awesome

Line Width Normalization

Overview

This code provides a pre-trained model of a part (line width normalization) of the research paper:

   Real-Time Data-Driven Interactive Rough Sketch Inking
   Edgar Simo-Serra, Satoshi Iizuka, Hiroshi Ishikawa
   ACM Transactions on Graphics (SIGGRAPH), 2018

See our project page for more detailed information.

Dependencies

• PyTorch torchvision
• pillow

All packages should be part of a standard PyTorch install. For information on how to install PyTorch please refer to the torch website.

Usage

python thin.py [infile] [outfile]

By default infile is set to "in.png" and outfile is set to "out.png".

Citing

If you use these models please cite:

@Article{SimoSerraSIGGRAPH2018,
   author    = {Edgar Simo-Serra and Satoshi Iizuka and Hiroshi Ishikawa},
   title     = {{Real-Time Data-Driven Interactive Rough Sketch Inking}},
   journal   = "ACM Transactions on Graphics (SIGGRAPH)",
   year      = 2018,
   volume    = 37,
   number    = 4,
}

Acknowledgements

This work was partially supported by JST CREST Grant Number JPMJCR14D1, and JST ACT-I Grant Numbers JPMJPR16UD and JPMJPR16U3.

License

The model weights are shared under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. See LICENSE for more information