# HairPort: In-context 3D-aware Hair Import and Transfer for Images

arXiv: https://arxiv.org/abs/2606.12562 · HTML: https://mlsanity.com/paper/2606.12562/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 62 references that are in the map.
- First posted on arXiv 10 Jun 2026.

## Builds on (most cited first, 30 of 32)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [MV-Adapter: Multi-view Consistent Image Generation Made Easy](https://mlsanity.com/paper/2412.03632/) (2024)
- [Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridging](https://mlsanity.com/paper/2503.22236/) (2025)
- [HairCLIP: Design Your Hair by Text and Reference Image](https://mlsanity.com/paper/2112.05142/) (2021)
- [SKED: Sketch-guided Text-based 3D Editing](https://mlsanity.com/paper/2303.10735/) (2023)
- [Zero-shot Image Editing with Reference Imitation](https://mlsanity.com/paper/2406.07547/) (2024)
- [Insert Anything: Image Insertion via In-Context Editing in DiT](https://mlsanity.com/paper/2504.15009/) (2025)
- [EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models](https://mlsanity.com/paper/2401.11739/) (2024)
- [SLiMe: Segment Like Me](https://mlsanity.com/paper/2309.03179/) (2023)
- [LOHO: Latent Optimization of Hairstyles via Orthogonalization](https://mlsanity.com/paper/2103.03891/) (2021)
- [Edit Transfer: Learning Image Editing via Vision In-Context Relations](https://mlsanity.com/paper/2503.13327/) (2025)
- [Few-Shot Head Swapping in the Wild](https://mlsanity.com/paper/2204.13100/) (2022)
- [h-Edit: Effective and Flexible Diffusion-Based Editing via Doob's h-Transform](https://mlsanity.com/paper/2503.02187/) (2025)
- [HairCLIPv2: Unifying Hair Editing via Proxy Feature Blending](https://mlsanity.com/paper/2310.10651/) (2023)
- [Barbershop: GAN-based Image Compositing using Segmentation Masks](https://mlsanity.com/paper/2106.01505/) (2021)
- [Zero-Shot Video Semantic Segmentation based on Pre-Trained Diffusion Models](https://mlsanity.com/paper/2405.16947/) (2024)
- [HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach](https://mlsanity.com/paper/2404.01094/) (2024)
- [HairFIT: Pose-Invariant Hairstyle Transfer via Flow-based Hair Alignment and Semantic-Region-Aware Inpainting](https://mlsanity.com/paper/2206.08585/) (2022)
- [What to Preserve and What to Transfer: Faithful, Identity-Preserving Diffusion-based Hairstyle Transfer](https://mlsanity.com/paper/2408.16450/) (2024)
- [SHeaP: Self-Supervised Head Geometry Predictor Learned via 2D Gaussians](https://mlsanity.com/paper/2504.12292/) (2025)
- [StyleGAN Salon: Multi-View Latent Optimization for Pose-Invariant Hairstyle Transfer](https://mlsanity.com/paper/2304.02744/) (2023)
- [Hair-GANs: Recovering 3D Hair Structure from a Single Image](https://mlsanity.com/paper/1811.06229/) (2018)

## Abstract (arXiv)

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.