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

1. Download your data to the dataset folder. You can use the provided download script to start off with the Zappos50K dataset.
2. 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:

1. Optimal transport
2. Our method
3. GAN linear interpolation

Some samples: