Skip to content

Latest commit

 

History

History
145 lines (120 loc) · 6.26 KB

README.md

File metadata and controls

145 lines (120 loc) · 6.26 KB
Raw

Mask Based Unsupervised Content Transfer (ICLR 2020)

PyTorch implementation of "Mask Based Unsupervised Content Transfer".

5 minutes video

Given two domains where one contains some additional information compared to the other, our method disentangles the common and the seperate parts and transfers the seperate information from one image to another using a mask, while not using any supervision at train time. For example, we can transfer facial hair from an image of a men with a mustache to an image of a shaved person, see image below. Using a mask enables state-of-the-art quality, but also, the generated mask can be used as a semantic segmentation of the seperate part. In addition, our method can also be used for removal.

For more details, please refer to the paper.

The first example presents facial hair transfer, where the facial hair of the men in the left column transferred to the shaved men in the upper row. The second example illustrate semantic segmentation of glasses and facial hair:

The second example illustrate semantic segmentation of glasses and facial hair:

Third example shows facial hair removal:

Prerequisites:

Python 2.7 / 3.6, Pytorch 0.4, argparse, Pillow

Download and Prepare the Data:

Download celebA directly or using this script.

Contrary to the notation used in the paper, A is the larger set, for example, A is people with glasses and B is people without. You can use the provided script preprocess.py to split celebA into the above format (with A and B based on the attribute of your choosing). For example, you can run the script using the following command (for Eyewear):

python preprocess.py --root ./img_align_celeba --attributes ./list_attr_celeba.txt --dest ./glasses_data --config glasses

Training

The Hyperparameters names are different then the article, the mapping between the codes name and the loss functions is:

  • alpha1 -
  • alpha2 -
  • beta1 - (first term)
  • beta2 - (second term)
  • delta -
  • gama -
  • discweight -

Train using mask_train.py. You can use the following example to run with default hyperparameters (for Eyewear):

python mask_train.py --root ./glasses_data --out ./glasses_experiment

For choosing GPU use

--gpu 3

You can use the following example to resume training:

python mask_train.py --root ./glasses_data --out ./glasses_experiment --load ./glasses_experiment

Evaluation

You can evalute with the command:

python mask_eval.py --root ./glasses_data --out ./glasses_experiment

Note that for evluate the segmentation, one should perform thresholding on the generated mask (search for the best threshold).

Hyperparameters:

Hyperparameters for Eyewear (glasses) are the defualt.

  • Hyperparameters for smile (male and female):
--lr 0.0002 --disclr 0.0002 --alpha1 1.0 --alpha2 1.0 --beta1 0.0 --beta2 0.0001 --gama 7.0 --delta 5.0 --discweight 0.005
  • Hyperparameters for women hair segmentation ("black to blond content transfer"):
--lr 0.0002 --disclr 0.0002 --alpha1 0.1 --alpha2 0.1 --beta1 0.0 --beta2 0.04 --gama 1.0 --delta 1.0 --discweight 1.0
  • Hyperparameters for men hair segmentation ("dark hair to bald content transfer", use the bald condig in preprocess.py):
--lr 0.0002 --disclr 0.0002 --alpha1 0.5 --alpha2 0.5 --beta1 0.0 --beta2 0.5 --gama 5.0 --delta 5.0 --discweight 1.0

Custom Dataset

First, You can then run the preprocessing in the following manner:

python preprocess.py --root ./custom_dataset --dest ./custom_train --folders

or use the following format for the images:

root/
     trainA/
     trainB/
     testA/
     testB/

Second, search for the best hyperparameters, as the default hyperparameters might not suit any dataset.

Removal

For facial hair removal: First, download pretrained model. Then run the command:

python removal.py --gpu 1 --eval_folder ./folder_to_eval/ --amount 64 --load ./ --check checkpoint_mustache --out ./out_folder/ --threshold 0.1 --ext '.png' --old_model True

Where "folder_to_eval" contain the images on which you wish to perform removel, "ext" is the file extension and "amount" is the amount of images you wish to evaluate, you can also use dataset similar to training by replacing "folder_to_eval" with:

--root ./root_folder/

(in this case "ext" and "amount" are not necessary).

If you wish to train the model by yourself please use

--old_model False

Our models

Facial hair pretrained model.

Eyewear pretrained model.

You can perform Eyewear evaluation using the command:

python mask_eval.py --root ./glasses_data --out ./gl_out/ --load ./ --check checkpoint_glasses --gpu 1 --old_model True

Acknowledgements

This implementation is heavily based on https://github.com/oripress/ContentDisentanglement.

Citation

If you found this work useful, please cite.

@article{abs-1906.06558,
  author    = {Ron Mokady and
               Sagie Benaim and
               Lior Wolf and
               Amit Bermano},
  title     = {Mask Based Unsupervised Content Transfer},
  journal   = {CoRR},
  volume    = {abs/1906.06558},
  year      = {2018},
  url       = {http://arxiv.org/abs/1906.06558},
  archivePrefix = {arXiv},
  eprint    = {1906.06558},
}

Contact

For further questions, ron.mokady@gmail.com .