Feature Disentanglement in GANs for Photorealistic Multi-view Hair Transfer
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Fast and highly realistic multi-view hair transfer plays a crucial role in evaluating the effectiveness of virtual hair try-on systems. However, GAN-based generation and editing methods face persistent challenges in feature disentanglement. Achieving pixel-level, attribute-specific modifications-such as changing hairstyle or hair color without affecting other facial features- remains a long-standing problem. To address this limitation, we propose a novel multi-view hair transfer framework that leverages a hair-only intermediate facial representation and a 3D-guided masking mechanism. Our approach disentangles triplane facial features into spatial geometric components and global style descriptors, enabling independent and precise control over hairstyle and hair color. By introducing a dedicated intermediate representation focused solely on hair and incorporating a two-stage feature fusion strategy guided by the generated 3D mask, our framework achieves fine-grained local editing across multiple viewpoints while preserving facial integrity and improving background consistency. Extensive experiments demonstrate that our method produces visually compelling and natural results in side-to-front view hair transfer tasks, offering a robust and flexible solution for high-fidelity hair reconstruction and manipulation.
Description
CCS Concepts: Computing methodologies → Computer graphics; Image manipulation; Image processing
@article{10.1111:cgf.70245,
journal = {Computer Graphics Forum},
title = {{Feature Disentanglement in GANs for Photorealistic Multi-view Hair Transfer}},
author = {Xu, Jiayi and Wu, Zhengyang and Zhang, Chenming and Jin, Xiaogang and Ji, Yaohua},
year = {2025},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.70245}
}