image_barycenters
This repository can be used to reproduced the results reported in our paper, accepted to CVPR 2020.
If you use our work, please cite it:
@InProceedings{Simon_2020_CVPR,
author = {Simon, Dror and Aberdam, Aviad},
title = {Barycenters of Natural Images Constrained Wasserstein Barycenters for Image Morphing},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
Training the model
- Download your data to the dataset folder. You can use the provided download script to start off with the Zappos50K dataset.
- Run train.py
Using Trained models
The networks folder contains a link for you to download models trained on the Zappos50K dataset.
Creating image transformations
Run generate_morph.py. If you do not mention specific images, random samples will be chosen from the provided dataset.
By default, the results will be saved to the "results" folder. Each output contains 3 rows in the following order:
- Optimal transport
- Our method
- GAN linear interpolation
Some samples:
