Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion from a Single-View Image (WACV 2022)
Yeong-Joon Ju, Gun-Hee Lee, Jung-Ho Hong, and Seong-Whan Lee
Below images are frontalization and de-occlusion results. The first rows are the input images and the second rows are our results. We crop the results as alignments for input images of facial recognition networks.
Training codes for occlusion robust 3D face reconstruction in this paper are available in here.
We present a self-supervision strategy called Swap-R&R to overcome the lack of ground-truth in a fully unsupervised manner for joint face rotation and de-occlusion. To generate an input pair for self-supervision, we transfer the occlusion from a face in an image to an estimated 3D face and create a damaged face image, as if rotated from a different pose by rotating twice with the roughly de-occluded face. Furthermore, we propose Complete Face Recovery GAN (CFR-GAN) to restore the collapsed textures and disappeared occlusion areas by leveraging the structural and textural differences between two rendered images. Unlike previous works, which have selected occlusion-free images to obtain ground-truths, our approach does not require human intervention and paired data.
Please read the document to the end before attempting with your images.
Our code was implemented in Ubuntu 16.04 and 18.04. So this code may not support any OS other than Linux OS
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Download our trained weights.
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Download BFM_model_80.mat and 3D face estimator weights here.
Generate de-occluded and rotated face images given a pose.
python inference.py --img_path test_imgs/input --save_path test_imgs/output --generator_path [saved_path] --estimator_path [saved_path]Generate training data pairs from your data!!
python generate_pair.py
Please check parameters of main in the code.
!! If you want to use your images, you should align images. Please refer to this repo for alignment. !!
Inference for non-aligned facial images
For alignment, You can use MTCNN or RetinaFace but we recommend to use RetinaFace.
git clone https://github.com/biubug6/Pytorch_Retinaface.git
mkdir Pytorch_Retinaface/weights
Download weights
python inference.py --img_path [your image path] --save_path [your save path] --generator_path [saved_path] --estimator_path [saved_path] --aligner retinaface@InProceedings{Ju_2022_WACV,
author = {Ju, Yeong-Joon and Lee, Gun-Hee and Hong, Jung-Ho and Lee, Seong-Whan},
title = {Complete Face Recovery GAN: Unsupervised Joint Face Rotation and De-Occlusion From a Single-View Image},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2022},
pages = {3711-3721}
}
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This work was supported by Institute of Information & communications Technology Planning Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program(Korea University))
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This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2019-0-01371, Development of brain-inspired AI with human-like intelligence).
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