https://github.com/bobbens/sketch_simplification
Models and code related to sketch simplification of rough sketches.
https://github.com/bobbens/sketch_simplification
convolutional-neural-networks deep-learning pytorch siggraph sketch torch
Last synced: about 1 month ago
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Models and code related to sketch simplification of rough sketches.
- Host: GitHub
- URL: https://github.com/bobbens/sketch_simplification
- Owner: bobbens
- License: other
- Created: 2017-04-07T04:02:53.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2022-01-13T01:19:26.000Z (over 3 years ago)
- Last Synced: 2025-03-28T19:11:28.268Z (about 2 months ago)
- Topics: convolutional-neural-networks, deep-learning, pytorch, siggraph, sketch, torch
- Language: Lua
- Homepage: https://esslab.jp/~ess/research/sketch_master/
- Size: 2.18 MB
- Stars: 738
- Watchers: 28
- Forks: 102
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# [Sketch Simplification](https://esslab.jp/~ess/research/sketch/)
Example result of a sketch simplification. Image copyrighted by Eisaku Kubonouchi ([@EISAKUSAKU](https://twitter.com/eisakusaku)) and only non-commercial research usage is allowed.
## Overview
This code provides pre-trained models used in the research papers:
```
"Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup"
Edgar Simo-Serra*, Satoshi Iizuka*, Kazuma Sasaki, Hiroshi Ishikawa (* equal contribution)
ACM Transactions on Graphics (SIGGRAPH), 2016
```
and
```
"Mastering Sketching: Adversarial Augmentation for Structured Prediction"
Edgar Simo-Serra*, Satoshi Iizuka*, Hiroshi Ishikawa (* equal contribution)
ACM Transactions on Graphics (TOG), 2018
```
See our [project page](https://esslab.jp/~ess/research/sketch_master/) for more detailed information.
## Dependencies
- [PyTorch](http://pytorch.org/) (version 0.4.1)
[torchvision](http://pytorch.org/docs/master/torchvision/)
- [pillow](http://pillow.readthedocs.io/en/latest/index.html)
All packages should be part of a standard PyTorch install. For information on how to install PyTorch please refer to the [torch website](http://pytorch.org/).
## Usage
Before the first usage, the models have to be downloaded with:
```
bash download_models.sh
```
Next test the models with:
```
python simplify.py
```
You should see a file called `out.png` created with the output of the model.
Application options can be seen with:
```
python simplify.py --help
```
## Pencil Drawing Generation
Using the same interface it is possible to perform pencil drawing generation. In this case, the input should be a clean line drawing and not a rough sketch, and the line drawings can be generated by:
```
python simplify.py --img test_line.png --out out_rough.png --model model_pencil2.t7
```
This will generate a rough version of `test_line.png` as `out_rough.png`. By changing the model it is possible to change the type of rough sketch being generated.
## Models
- `model_mse.t7`: Model trained using only MSE loss (SIGGRAPH 2016 model).
- `model_gan.t7`: Model trained with MSE and GAN loss using both supervised and unsupervised training data (TOG 2018 model).
- `model_pencil1.t7`: Model for pencil drawing generation based on artist 1 (dirty and faded pencil lines).
- `model_pencil2.t7`: Model for pencil drawing generation based on artist 2 (clearer overlaid pencil lines).
## Reproducing Paper Figures
For replicability we include code to replicate the figures in the paper. After downloading the models you can run it with:
```
./figs.sh
```
This will convert the input images in `figs/` and save the output in `out/`. We note that there are small differences with the results in the paper due to hardware differences and small differences in the torch/pytorch implementations. Furthermore, results are shown without the post-processing mentioned in the notes at the bottom of this document.
Please note that we do not have the copyright for all these images and in general only non-commercial research usage is permitted. In particular, `fig16_eisaku.png`, `fig06_eisaku_robo.png`, `fig06_eisaku_joshi.png`, and `fig01_eisaku.png` are copyright by Eisaku Kubonoichi ([@EISAKUSAKU](https://twitter.com/eisakusaku)) and only non-commercial research usage is allowed.
The images`fig14_pepper.png` and `fig06_pepper.png` are licensed by David Revoy ([www.davidrevoy.com](http://www.davidrevoy.com/)) under CC-by 4.0.
## Training
Please see the [training readme](train/TRAIN.md).
## Notes
- Models are in Torch7 format and loaded using the PyTorch legacy code.
- This was developed and tested on various machines from late 2015 to end of 2016.
- Provided models are under a non-commercial creative commons license.
- Post-processing is not performed. You can perform it manually with `convert out.png bmp:- | mkbitmap - -t 0.3 -o - | potrace --svg --group -t 15 -o - > out.svg`.
## Citing
If you use these models please cite:
```
@Article{SimoSerraSIGGRAPH2016,
author = {Edgar Simo-Serra and Satoshi Iizuka and Kazuma Sasaki and Hiroshi Ishikawa},
title = {{Learning to Simplify: Fully Convolutional Networks for Rough Sketch Cleanup}},
journal = "ACM Transactions on Graphics (SIGGRAPH)",
year = 2016,
volume = 35,
number = 4,
}
```
and
```
@Article{SimoSerraTOG2018,
author = {Edgar Simo-Serra and Satoshi Iizuka and Hiroshi Ishikawa},
title = {{Mastering Sketching: Adversarial Augmentation for Structured Prediction}},
journal = "ACM Transactions on Graphics (TOG)",
year = 2018,
volume = 37,
number = 1,
}
```
## Acknowledgements
This work was partially supported by JST CREST Grant Number JPMJCR14D1 and JST ACT-I Grant Numbers JPMJPR16UD and JPMJPR16U3.
## License
This sketch simplification code is freely available for free non-commercial
use, and may be redistributed under these conditions. Please, see the [license](/LICENSE)
for further details.