Transferring hairstyles between images is an important but challenging task in computer graphics, computer vision, and visual effects. It enables users to explore new looks without physically altering their hair, with applications in virtual try-on systems, augmented reality, and entertainment. Most prior works operate best under small pose gaps, and they fall short under large viewpoint and scale differences, where missing hair content must be synthesized rather than transferred. We propose HairPort, a 3D-aware hairstyle transfer framework that attempts to solve these issues by explicitly separating hair removal from transfer and enforcing geometric consistency before synthesis. We introduce a Bald Converter, which produces realistic bald versions of faces through LoRA-based in-context adaptation of FLUX.1 Kontext. To train our Bald Converter, we introduce a new dataset, Baldy, containing 6,000 paired bald and original images across diverse identities and conditions. We also use a 3D-Aware Transfer Pipeline that reconstructs and re-renders the reference hairstyle from the target viewpoint before compositing it onto the source image. Being 3D aware, our method supports large pose and scale discrepancies between the source and target. Finally, a conditional flow-matching generator synthesizes the transferred result from the bald source and geometry-aligned reference guidance. Together, our method enables accurate, pose-consistent, and identity-preserving hairstyle transfer, outperforming existing methods both qualitatively and quantitatively.

翻译：摘要：发型迁移是计算机图形学、计算机视觉及视觉特效领域中一项重要但具有挑战性的任务。该技术使用户无需实际改变自身发型即可探索新造型，在虚拟试穿系统、增强现实及娱乐领域具有广泛应用。现有方法大多仅适用于小姿态差异场景，当面临大幅视角和尺度变化时表现不佳——此时缺失的发型内容需要通过合成而非迁移实现。我们提出HairPort，一种三维感知的发型迁移框架，通过显式分离发型移除与迁移过程，并在合成前强制几何一致性来尝试解决上述问题。创新性地引入秃头转换器，基于FLUX.1 Kontext模型的LoRA上下文自适应技术，生成面部真实秃头版本。为训练该秃头转换器，我们构建了新数据集Baldy，包含6000对涵盖多样身份与条件的人脸秃头/原图配对数据。同时采用三维感知迁移流水线，将参考发型从目标视角重建并重新渲染后，再与源图像进行合成。得益于三维感知特性，本方法支持源图像与参考发型之间存在大幅姿态与尺度差异。最终，条件流匹配生成器通过结合秃头源图像与几何对齐的参考引导，合成发型迁移结果。本方法在保持身份特征的前提下实现了精准、姿态一致的发型迁移，在定性与定量评估中均优于现有方法。