tchambon / A-Sliced-Wasserstein-Loss-for-Neural-Texture-Synthesis

Official implementation of "A Sliced Wasserstein Loss for Neural Texture Synthesis" paper (CVPR 2021).

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)

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.