HairCLIP: Design Your Hair by Text and Reference Image (CVPR2022)

This repository hosts the official PyTorch implementation of the paper: "HairCLIP: Design Your Hair by Text and Reference Image".

Our single framework supports hairstyle and hair color editing individually or jointly, and conditional inputs can come from either image or text domain.

Tianyi Wei¹, Dongdong Chen², Wenbo Zhou¹, Jing Liao³, Zhentao Tan¹, Lu Yuan², Weiming Zhang¹, Nenghai Yu^1
1University of Science and Technology of China, ²Microsoft Cloud AI, ³City University of Hong Kong

News

2022.03.02: Our paper is accepted by CVPR2022 and the code will be released soon.
2022.03.09: Our training code is released.

Abstract

Hair editing is an interesting and challenging problem in computer vision and graphics. Many existing methods require well-drawn sketches or masks as conditional inputs for editing, however these interactions are neither straightforward nor efficient. In order to free users from the tedious interaction process, this paper proposes a new hair editing interaction mode, which enables manipulating hair attributes individually or jointly based on the texts or reference images provided by users. For this purpose, we encode the image and text conditions in a shared embedding space and propose a unified hair editing framework by leveraging the powerful image text representation capability of the Contrastive Language-Image Pre-Training (CLIP) model. With the carefully designed network structures and loss functions, our framework can perform high-quality hair editing in a disentangled manner. Extensive experiments demonstrate the superiority of our approach in terms of manipulation accuracy, visual realism of editing results, and irrelevant attribute preservation.

Comparison

Comparison to Text-Driven Image Manipulation Methods

Comparison to Hair Transfer Methods

Application

Hair Interpolation

Generalization Ability to Unseen Descriptions

Cross-Modal Conditional Inputs

To Do

• Release training code
• Release testing code
• Release pretrained model

Citation

If you find our work useful for your research, please consider citing the following papers :)

@article{wei2022hairclip,
  title={Hairclip: Design your hair by text and reference image},
  author={Wei, Tianyi and Chen, Dongdong and Zhou, Wenbo and Liao, Jing and Tan, Zhentao and Yuan, Lu and Zhang, Weiming and Yu, Nenghai},
  journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}