TimZhang001 / GPDM

Original Pytorch implementation of the GPDM model introduced in "Generating natural images with direct patch distributions matching"

GPDM

Original Pytorch implementation of the GPDM algorithm introduced in

"Generating Natural Images with Direct Patch Distribution Matching"

Accepted to ECCV 2022

Live-demo | Paper

Video presentation

Run GPDM:

Reshuffling

$ python3 main.py data/images/SIGD16/7.jpg

Input	Output

Retargeting

$ python3 main.py data/images/SIGD16/4.jpg --init_from target --width_factor 1.5

Input	Output

Style transfer

$ python3 main.py data/images/style_transfer/style/mondrian.jpg --init_from data/images/style_transfer/content/trump.jpg --fine_dim 1024 --coarse_dim 256 --noise_sigma 0

Input	init_from	Output

Texture synthesis

$ python3 main.py data/images/textures/cobbles.jpeg --width_factor 1.5 --height_factor 1.5

Input	Output

Reproduce paper tables

I added the Places50 and SIGD16 datasets from Drop-The-Gan and SinGAN so that results can be reproduced

Apart from the datasets from the paper I collected some interesting retargeting images in the images folder

In the images folder you can find images I collected from various repos and papers cited in my paper.

Cite

@inproceedings{elnekave2022generating,
  title={Generating natural images with direct Patch Distributions Matching},
  author={Elnekave, Ariel and Weiss, Yair},
  booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XVII},
  pages={544--560},
  year={2022},
  organization={Springer}
}