HairCLIP: Design Your Hair by Text and Reference Image

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Deep LearningHairCLIP
Overview

Overview

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 Yu1
1University of Science and Technology of China, 2Microsoft Cloud AI, 3City University of Hong Kong

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 testing code
  • Release pretrained model
  • Release training code
Owner
Ph.D Student @ University of Science and Technology of China
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