[2606.12562] HairPort: In-context 3D-aware Hair Import and Transfer for Images
[Submitted on 10 Jun 2026]
Title:HairPort: In-context 3D-aware Hair Import and Transfer for Images
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Abstract: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.
Comments: Accepted to SIGGRAPH 2026 (Conference Papers Track). 23 pages, 15 figures, 10 tables, including supplementary material as appendices. Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
ACM classes: I.3.3; I.3.7; I.4.9; I.2.6
Cite as: arXiv:2606.12562 [cs.CV]
(or arXiv:2606.12562v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.12562
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1145/3799902.3811046
DOI(s) linking to related resources
Submission history
From: Alireza Heidari [view email] [v1] Wed, 10 Jun 2026 18:13:18 UTC (10,087 KB)
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