HRFAE: High Resolution Face Age Editing

Official implementation for paper High Resolution Face Age Editing.

Dependencies

• Python 3.7
• Pytorch 1.1
• Numpy
• Opencv
• TensorboardX
• Tensorboard_logger

You can also create a new environment for this repo by running

conda env create -f env.yml

Load and test pretrained network

1. You can download the pretrained model by running:

   cd ./logs/001
   ./download.sh
   

2. Upload test images in the folder

   /test/input

   and run the test file. The output images will be saved in the folder

   /test/output

   . You can change the desired target age with

   --target_age

   .

   python test.py --config 001 --target_age 65
   

Train a new model

1. Pretrained age classifier

   To get age information, we use an age classifier pretrained on IMDB-WIKI dataset. We use the model released from paper Deep expectation of real and apparent age from a single image without facial landmarks by Rothe et al.

   To prepare the model, you need to download the original caffe model and convert it to PyTorch format. We use the converter caffemodel2pytorch released by Vadim Kantorov. Then name the PyTorch model as

   dex_imdb_wiki.caffemodel.pt

   and put it in the folder

   /models

   .

2. Preparing your dataset

   Download FFHQ dataset and unzip it to the

   /data/ffhq

   directory.

   Download age label to the

   /data

   directory.

   You can also train the model with your own dataset. Put your images in the

   /data

   directory. With the pretrained classifier, you can create a new label file with the age of each image.

3. Training

   You can modify the training options of the config file in

   configs

   directory.

   python train.py --config 001 
   

Google Colab

We also provide a colab version for quick test. To run it using Google Colab, please click here.

Citation

@article{yao2020high,
    title   = {High Resolution Face Age Editing},
    author  = {Xu Yao and Gilles Puy and Alasdair Newson and Yann Gousseau and Pierre Hellier},
    journal = {CoRR},
    volume  = {abs/2005.04410},
    year    = {2020},
}

License

Copyright © 2020, InterDigital R&D France. All rights reserved.

This source code is made available under the license found in the LICENSE.txt in the root directory of this source tree.