A Sliced Wasserstein Loss for Neural Texture Synthesis - PyTorch version
This is an unofficial, refactored PyTorch implementation of "A Sliced Wasserstein Loss for Neural Texture Synthesis" paper [CVPR 2021].
Notice:
- The customized VGG-19 architecture might be different from the original Tensorflow implementation. Thus, some results might be inconsistent to the paper. Feel free to give advice.
- The spatial tag part is not included in this implementation.
Prerequisites
- Python 3.7.10
- PyTorch 1.9.0
Data
I have collected data in the data folder from the official repository and from "Deep Correlations for Texture Synthesis
" [Siggraph 2017].
Run
First cd pytorch and then run some random examples:
python texturegen.py --data_folder=SlicedW --img_name=input.jpg
python texturegen.py --data_folder=SlicedW --img_name=2.png
python texturegen.py --data_folder=SlicedW --img_name=berry.png
python texturegen.py --data_folder=SlicedW --img_name=64.png
python texturegen.py --data_folder=DCor --img_name=Texture13.png
python texturegen.py --data_folder=DCor --img_name=Texture32.jpg
python texturegen.py --data_folder=DCor --img_name=Texture19.png
python texturegen.py --data_folder=DCor --img_name=Texture22.png
After slightly more than 1 minute for each scene, you can find intermediate outputs in outputs folder, and final results in results folder.
Sample Results
| Input | Synthesized |
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