https://github.com/blade6570/icface

ICface: Interpretable and Controllable Face Reenactment Using GANs
https://github.com/blade6570/icface

action-units animation deeplearning face ganimation gans image-generation pytorch video-generation

Last synced: about 2 months ago
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ICface: Interpretable and Controllable Face Reenactment Using GANs

• Host: GitHub
• URL: https://github.com/blade6570/icface
• Owner: Blade6570
• License: other
• Created: 2019-04-03T09:46:44.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2020-07-10T17:08:48.000Z (about 5 years ago)
• Last Synced: 2025-05-12T23:44:48.315Z (5 months ago)
• Topics: action-units, animation, deeplearning, face, ganimation, gans, image-generation, pytorch, video-generation
• Language: Python
• Size: 12.8 MB
• Stars: 162
• Watchers: 12
• Forks: 26
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • License: License

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README

          
## ICface: Interpretable and Controllable Face Reenactment Using GANs ##

**Accepted WACV-2020**

[**Project**](https://tutvision.github.io/icface/) | [**Youtube**](https://www.youtube.com/watch?v=VhWrAjI6z0M) | [**Arxiv**](https://arxiv.org/abs/1904.01909)

***

![](https://github.com/Blade6570/icface/blob/master/images/tease.png?raw=true "teaser image")

**Single Image Animation**

Source Image-1 | Animation-1|Source Image-2 | Animation-2

:---------:|:----------:|:---------:|:----------:

 ![](https://github.com/Blade6570/icface/blob/master/test_code_released_new/new_crop/7.jpg?raw=true "teaser gif1") |![](https://github.com/Blade6570/icface/blob/master/images/kim.gif?raw=true "teaser gif1") | ![](https://github.com/Blade6570/icface/blob/master/test_code_released_new/new_crop/8.png?raw=true "teaser gif1") |![](https://github.com/Blade6570/icface/blob/master/images/trump.gif?raw=true "teaser gif1")               

This is the part of the implementation of "ICface: Interpretable and Controllable Face Reenactment Using GANs" (https://arxiv.org/abs/1904.01909). 

~~_The test code is released now!_~~

:white_check_mark: **_test_code is updated_**

`**Note**: The implementation and results have improved with the current modifications. The codes are updated in /test_code_released_new/. The checkpoints are also updated and added. Feel free to raise an issue if you face any difficulties in running the updated files.`

**Prerequisites**

1. Python 3.5.4

2. Pytorch 0.4.1

3. Visdom and dominate 

4. Natsort

*The code is tested on Ubuntu 16.04 LTS* 

**Download the Pretrained Weights of ICface**

[Google Drive Link](https://drive.google.com/drive/folders/1jvD8R-Ggo3Seg2tF-JsqlOjwropVwN1S?usp=sharing). 

**Testing ICface**

1. Clone the ICface repository and change the working directory to *'/test_code_released_new'* 

2. Keep the pretrained weights inside the path: *./checkpoints/gpubatch_resnet*. 

3. For the driving video, you can select any video file from *voxceleb* dataset, extract the action units in a .csv file using Openface and store the .csv file in the working folder. We have provided two such .csv files and thier corresponding driving videos.

4. For the source image, we have selected images from *voxceleb* test set. Three exampes are given in the folder *./new_crop*. More can be obtained from [here](http://www.robots.ox.ac.uk/~vgg/research/CMBiometrics/). In particular the "Cropped Face Images extracted at 1fps" (7.8Gb). The test identities can be downloaded here under the data section.

5. Run in terminal : *python test.py --dataroot ./ --model pix2pix --which_model_netG resnet_6blocks --which_direction AtoB --dataset_mode aligned --norm batch --display_id 0 --batchSize 1 --loadSize 128 --fineSize 128 --no_flip --name gpubatch_resnet --how_many 1 --ndf 256 --ngf 128 --which_ref ./new_crop/1.png --gpu_ids 1 --csv_path 00116.csv --results_dir results_video*

6. The resuting video will be found in *'/test_code_released_new'* under the name *'movie.mp4'*

**If you are not using *voxceleb* test set**

1. In the python file *'image_crop.py'*, add your image path and run it.

2. It will create a new cropped version of your image and will store in *'./new_crop'* folder. Then follow the above steps to create youe video file. 

-**_If you are using this implementation for your research work then please cite us as:_**

 

```

#Citation 

@article{tripathy+kannala+rahtu,

  title={ICface: Interpretable and Controllable Face Reenactment Using GANs},

  author={Tripathy, Soumya and Kannala, Juho and Rahtu, Esa},

  journal={arXiv preprint arXiv:1904.01909},

  year={2019}

}

```

*NOTE:* Code framework is based on [pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)