This is the official code of paper Non-staionary texture synthesis using adversarial expansions.
This code was mainly adapted by Zhen Zhu on the basis of the repository CycleGAN.
If you use this code for your research, please cite:
Non-staionary texture synthesis using adversarial expansions
Yang Zhou*, Zhen Zhu*, Xiang Bai, Dani Lischinski, Daniel Cohen-Or, Hui Huang
In SIGGRAPH 2018. (* equal contributions)
The total project can be well functioned under the following environment:
- python-2.7
- pytorch-0.3.0 with cuda correctly specified
- cuda-8.0
- other packages under python-2.7
Please run download_pretrained_models first to make a new folder Models and then download the VGG19 model pretrained on ImageNet to this folder.
There is no restriction for the format of the source texture images. The structure of the data folder is recommanded as the provided sub-folders inside datasets folder. To be more specific, datasets/half is what we use in paper production.
The dataset structure is recommended as:
+--half
|
| +--sunflower
|
| +--train
|
| +--sunflower.jpg
|
| +--test
|
| +--sunflower.jpg
|
| +--brick
|
| +--train
|
| +--brick.jpg
|
| +--test
|
| +--brick.jpg
|
...
Inside the main folder, train.py is used to train a model as described in our paper. test.py is used to test with the original image(result is 2x the size of the input). test_recurrent.py is used for extreme expansions.
In folder data, file custom_dataset_data_loader specified five dataset mode: aligned, unaligned, single and half_crop. Generally, we use single for testing and half_crop for training.
In folder models, two files are of great importance: models.py and networks.py, please carefully check it before using it. half_gan_style.py is the major model we use in our paper. Some utilities are implemented in vgg.py.
In folder options, all hyperparameters are specified here. If you want to specialize different types of experiments, please change them accordingly.
Folder scripts contains scripts used for training and testing. To train or test a model, use commands like sh scripts/train_half_style.sh. Go into these files to see how to specify some hyper parameters. production scripts are typically used in production period in our paper preparation.
Folder util contains some scripts to generate perlin noise (perlin2d.py), generate random tile (random_tile.py), which are useful to replicate our paper's results. Some other useful scripts are also included.
Folder scripts contain scripts used for training and testing. To train or test a model, use commands like sh scripts/train_half_style.sh. Go into these files to see how to specify some hyper parameters. To visualize the internal layers inside network, especially the residual blocks, you can use script visualize_layers.sh, as shown in our paper.
If you use this code for your research, please cite our paper:
@article{TexSyn18,
title = {Non-stationary Texture Synthesis by Adversarial Expansion},
author = {Yang Zhou and Zhen Zhu and Xiang Bai and Dani Lischinski and Daniel Cohen-Or and Hui Huang},
journal = {ACM Transactions on Graphics (Proc. SIGGRAPH)},
volume = {37},
number = {4},
pages = {},
year = {2018},
}
The code is based on project CycleGAN. We sincerely thank for their great work.