# Hair-GANs: Recovering 3D Hair Structure from a Single Image

arXiv: https://arxiv.org/abs/1811.06229 · HTML: https://mlsanity.com/paper/1811.06229/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Stable-Hair: Real-World Hair Transfer via Diffusion Model](https://mlsanity.com/paper/2407.14078/) (2024)
- [Im2Haircut: Single-view Strand-based Hair Reconstruction for Human Avatars](https://mlsanity.com/paper/2509.01469/) (2025)
- [A Local Appearance Model for Volumetric Capture of Diverse Hairstyle](https://mlsanity.com/paper/2312.08679/) (2023)
- [Stable-Hair v2: Real-World Hair Transfer via Multiple-View Diffusion Model](https://mlsanity.com/paper/2507.07591/) (2025)
- [Unsupervised 3D Braided Hair Reconstruction from a Single-View Image](https://mlsanity.com/paper/2506.23072/) (2025)
- [HairPort: In-context 3D-aware Hair Import and Transfer for Images](https://mlsanity.com/paper/2606.12562/) (2026)

## Abstract (arXiv)

We introduce Hair-GANs, an architecture of generative adversarial networks, to recover the 3D hair structure from a single image. The goal of our networks is to build a parametric transformation from 2D hair maps to 3D hair structure. The 3D hair structure is represented as a 3D volumetric field which encodes both the occupancy and the orientation information of the hair strands. Given a single hair image, we first align it with a bust model and extract a set of 2D maps encoding the hair orientation information in 2D, along with the bust depth map to feed into our Hair-GANs. With our generator network, we compute the 3D volumetric field as the structure guidance for the final hair synthesis. The modeling results not only resemble the hair in the input image but also possesses many vivid details in other views. The efficacy of our method is demonstrated by using a variety of hairstyles and comparing with the prior art.