A Sliced Wasserstein Loss for Neural Texture Synthesis
This is the official implementation of "A Sliced Wasserstein Loss for Neural Texture Synthesis" paper (CVPR 2021).
| Input | Output (SCALING_FACTOR=2) |
|---|---|
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If you use this work, please cite our paper
@InProceedings{Heitz_2021_CVPR,
author = {Heitz, Eric and Vanhoey, Kenneth and Chambon, Thomas and Belcour, Laurent},
title = {A Sliced Wasserstein Loss for Neural Texture Synthesis},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021}
}This implementation focuses on the key part of the paper: the sliced wasserstein loss for texture synthesis.
Requirements
Librairies
The following libraries are required:
- pytorch
- numpy
- imageio
This code has been tested with Python 3.7.5 on Ubuntu 18.04. We recommend setting up a dedicated Conda environment using Python 3.7.
Pretrained vgg-19 network
A custom vgg network is used, as explained in the supplementals. It has been modified compared to the pytorch standard model:
- inputs are preprocessed (including normalization with imagenet stats).
- activations are scaled.
- max pooling layers are replaced with average pooling layers.
- zero padding is remplaced with reflect padding.
Texture generation
To generate a texture use the following command:
python texture_optimization_slicing.py
The parameters inside the file texture_optimization_slicing.py are:
- FILE_PATH: Target texture file.
- SCALING_FACTOR: Scaling factor of the optimized texture in regards to the example texture (set to 1 to keep the same size, 2 to double the resolution).
Outputs files are:
- optimized_texture_N_iterations.png: the intermediate result after N iterations. If there are 20 iterations, there will be 20 output images.
Timing
Timing reference for 10 iterations on 256x256 resolution:
- NVIDIA Titan RTX: 1min05.