Andrew Hou, Michel Sarkis, Ning Bi, Yiying Tong, Xiaoming Liu. In CVPR, 2022.
The code for this project was developed using Python 3.8.8 and PyTorch 1.7.1.
The training data can be downloaded from: https://drive.google.com/file/d/1Jh4a5zvx92NRjC5E_MyaKMbFymx2Iw9E/view?usp=sharing
The original CelebAHQ dataset can be downloaded from: https://github.com/switchablenorms/CelebAMask-HQ
These images will need to be cropped and will go into their own folder MP_data/CelebA-HQ_DFNRMVS_cropped/
For the cropping code specifically, we will need to create a separate conda environment to ensure that the images are cropped consistently to match the rest of the training data (albedo, depth maps, etc.) Note that this conda environment is different from the conda environment we will use for training and testing.
To set up the conda environment for cropping the CelebA-HQ images, run the following:
conda create --name CelebAHQCrop --file cropping_dependencies.txt
conda activate CelebAHQCrop
pip install scipy
Once this environment is set up, place all of the CelebAHQ images that you downloaded in a directory named input_image_dir/ and make a second directory called output_image_dir/ which will store the cropped images. To crop the images, run the following command: CUDA_VISIBLE_DEVICES=0 python recrop_CelebA-HQ_images.py
conda create --name GeomShadows --file training_dependencies.txt
conda activate GeomShadows
pip3 install opencv-python
pip3 install kornia==0.4.1
pip install scipy
pip install imageio
Once you've downloaded all of the training data from our google drive link (albedo, depth, etc.) and finished cropping the CelebAHQ images as described above, move the cropped CelebAHQ images from output_image_dir/ to MP_data/CelebA-HQ_DFNRMVS_cropped/
To train the target lighting model, where the user can specify the desired lighting direction, make two additional directories to store the loss values and saved model weights: losses_raytracing_relighting_CelebAHQ_DSSIM_8x/ and saved_epochs_raytracing_relighting_CelebAHQ_DSSIM_8x/
Finally, you can train the target lighting model using the following command: CUDA_VISIBLE_DEVICES=0 python train_raytracing_relighting_CelebAHQ_DSSIM_8x.py
If you instead would like to train the lighting transfer model, make the following two directories instead: losses_lighting_transfer/ and saved_epochs_lighting_transfer/
To train the lighting transfer model, use the following command: CUDA_VISIBLE_DEVICES=0 python train_lighting_transfer.py
Be sure to use the correct conda environment (GeomShadows) for training.
To run our testing code where the user specifies the target lighting, use the following command: CUDA_VISIBLE_DEVICES=0 python test_relight_single_image.py
You can specify both an input image and a target lighting direction within the code (we provide some sample lightings to generate the results in FFHQ_relighting_results/). You will also need a face mask for the input image (some examples are shown in FFHQ_skin_masks/). To generate a face mask, one option is to use the following repo: https://github.com/zllrunning/face-parsing.PyTorch
Once a relit image is generated, use our fix_border_artifacts_CVPR2022.m postprocessing code to improve the face boundary if there are any dark or black pixels that appear along the boundary. This can sometimes happen as our method relights the face region only, and the boundary between the relit face and the original image may need to be smoothed.
To run our testing code for lighting transfer, first make a directory called lighting_transfer_results/ and then use the following command: CUDA_VISIBLE_DEVICES=0 python test_relight_single_image_lighting_transfer.py input_image reference_image face_mask
Be sure to use the correct conda environment (GeomShadows) for testing.
If you utilize our code in your work, please cite our CVPR 2022 paper.
@inproceedings{ face-relighting-with-geometrically-consistent-shadows,
author = { Andrew Hou and Michel Sarkis and Ning Bi and Yiying Tong and Xiaoming Liu },
title = { Face Relighting with Geometrically Consistent Shadows },
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = { 2022 }
}
If there are any questions, please feel free to post here or contact the first author at houandr1@msu.edu