Paper Info
Title
HairCLIP: Design Your Hair by Text and Reference Image
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 straight-forward 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.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.01754
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Vision + language, Image and video synthesis and generation, Vision applications and systems
Conference
2022
Issue
Citations 
PageRank 
1
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Tianyi Wei100.68
Dongdong Chen25219.10
Wenbo Zhou3296.28
Jing Liao418225.81
Zhentao Tan582.85
Lu Yuan680148.29
Weiming Zhang763.81
Nenghai Yu82238183.33