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Style-Your-Hair

Official Pytorch implementation of "Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment (ECCV 2022)"

teaser qualitative result

Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment<br/> Taewoo Kim*, Chaeyeon Chung*, Yoonseo Kim*, Sunghyun Park, Kangyeol Kim, and Jaegul Choo<br/> * indicates equal contributions.

arXiv | BibTeX |

Abstract Editing hairstyle is unique and challenging due to the complexity and delicacy of hairstyle. Although recent approaches significantly improved the hair details, this is achieved under the assumption that a target hair and a source image are aligned. HairFIT, a pose-invariant hairstyle transfer model, alleviates this assumption, yet it still shows unsatisfactory quality in preserving delicate hair textures. To solve these limitations, we propose a high-performing pose-invariant hairstyle transfer model equipped with a latent optimization and a newly presented local-style-matching loss. In the StyleGAN2 latent space, we first explore a pose-aligned latent code of a target hair with the detailed textures preserved based on local-style-matching. Then, our model inpaints the occlusions of the source considering the aligned target hair and blends both images to produce a final output. The experimental results demonstrate that our model has strengths in transferring a hairstyle under higher pose differences and preserving local hairstyle textures.

Description

Official Implementation of Style Your Hair. KEEP UPDATING! Please Git Pull the latest version.

Installation

Clone the repository:

git clone https://github.com/Taeu/Style-Your-Hair.git
cd Style-Your-Hair

Install dependencies:

conda create -n {env_name} python=3.7.9
conda activate {env_name}
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install face_alignment face-recognition gdown ipython matplotlib

Download example images

Please download the example images. And put the images in ./ffhq_image/ folder.

Getting Started

Produce the results:

python main.py --input_dir ./ffhq_image/ --im_path1 source.png --im_path2 target.png \
    --output_dir ./style_your_hair_output/ \
    --warp_loss_with_prev_list delta_w style_hair_slic_large \
    --save_all --version final --flip_check

Acknowledgments

This code borrows heavily from Barbershop.

BibTeX

@article{kim2022style,
  title={Style Your Hair: Latent Optimization for Pose-Invariant Hairstyle Transfer via Local-Style-Aware Hair Alignment},
  author={Kim, Taewoo and Chung, Chaeyeon and Kim, Yoonseo and Park, Sunghyun and Kim, Kangyeol and Choo, Jaegul},
  journal={arXiv preprint arXiv:2208.07765},
  year={2022}
}

License

Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).