Feature Disentanglement in GANs for Photorealistic Multi‐view Hair Transfer

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 tri‐plane 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.