# NeuralHDHair: Automatic High-fidelity Hair Modeling from a Single Image Using Implicit Neural Representations

arXiv: https://arxiv.org/abs/2205.04175 · HTML: https://mlsanity.com/paper/2205.04175/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 41 references that are in the map.
- First posted on arXiv 9 May 2022.

## Builds on (most cited first, 18 of 18)

- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction](https://mlsanity.com/paper/2104.10078/) (2021)
- [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [Local Implicit Grid Representations for 3D Scenes](https://mlsanity.com/paper/2003.08981/) (2020)
- [DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction](https://mlsanity.com/paper/1905.10711/) (2019)
- [Atlas: End-to-End 3D Scene Reconstruction from Posed Images](https://mlsanity.com/paper/2003.10432/) (2020)
- [Tex2Shape: Detailed Full Human Body Geometry From a Single Image](https://mlsanity.com/paper/1904.08645/) (2019)
- [Controlling Neural Level Sets](https://mlsanity.com/paper/1905.11911/) (2019)
- [Deep Exemplar-based Video Colorization](https://mlsanity.com/paper/1906.09909/) (2019)
- [360-Degree Textures of People in Clothing from a Single Image](https://mlsanity.com/paper/1908.07117/) (2019)
- [DeepSketchHair: Deep Sketch-based 3D Hair Modeling](https://mlsanity.com/paper/1908.07198/) (2019)
- [HairNet: Single-View Hair Reconstruction using Convolutional Neural Networks](https://mlsanity.com/paper/1806.07467/) (2018)
- [SketchHairSalon: Deep Sketch-based Hair Image Synthesis](https://mlsanity.com/paper/2109.07874/) (2021)

## Built on by (most cited first, 30 of 32)

- [SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting](https://mlsanity.com/paper/2403.05087/) (2024)
- [Gen3DSR: Generalizable 3D Scene Reconstruction via Divide and Conquer from a Single View](https://mlsanity.com/paper/2404.03421/) (2024)
- [GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians](https://mlsanity.com/paper/2402.10483/) (2024)
- [Neural Haircut: Prior-Guided Strand-Based Hair Reconstruction](https://mlsanity.com/paper/2306.05872/) (2023)
- [Text-Guided Generation and Editing of Compositional 3D Avatars](https://mlsanity.com/paper/2309.07125/) (2023)
- [HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling](https://mlsanity.com/paper/2303.02700/) (2023)
- [NeuWigs: A Neural Dynamic Model for Volumetric Hair Capture and Animation](https://mlsanity.com/paper/2212.00613/) (2022)
- [HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles](https://mlsanity.com/paper/2312.11666/) (2023)
- [MonoHair: High-Fidelity Hair Modeling from a Monocular Video](https://mlsanity.com/paper/2403.18356/) (2024)
- [Perm: A Parametric Representation for Multi-Style 3D Hair Modeling](https://mlsanity.com/paper/2407.19451/) (2024)
- [Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization](https://mlsanity.com/paper/2305.03043/) (2023)
- [DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models](https://mlsanity.com/paper/2505.06166/) (2025)
- [Automatic Animation of Hair Blowing in Still Portrait Photos](https://mlsanity.com/paper/2309.14207/) (2023)
- [Dr.Hair: Reconstructing Scalp-Connected Hair Strands without Pre-training via Differentiable Rendering of Line Segments](https://mlsanity.com/paper/2403.17496/) (2024)
- [Quaffure: Real-Time Quasi-Static Neural Hair Simulation](https://mlsanity.com/paper/2412.10061/) (2024)
- [Make-A-Character: High Quality Text-to-3D Character Generation within Minutes](https://mlsanity.com/paper/2312.15430/) (2023)
- [Human 3D Avatar Modeling with Implicit Neural Representation: A Brief Survey](https://mlsanity.com/paper/2306.03576/) (2023)
- [StrandHead: Text to Hair-Disentangled 3D Head Avatars Using Human-Centric Priors](https://mlsanity.com/paper/2412.11586/) (2024)
- [SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting](https://mlsanity.com/paper/2307.14489/) (2023)
- [A Local Appearance Model for Volumetric Capture of Diverse Hairstyle](https://mlsanity.com/paper/2312.08679/) (2023)
- [HairDiffusion: Vivid Multi-Colored Hair Editing via Latent Diffusion](https://mlsanity.com/paper/2410.21789/) (2024)
- [SRM-Hair: Single Image Head Mesh Reconstruction via 3D Morphable Hair](https://mlsanity.com/paper/2503.06154/) (2025)
- [DGH: Dynamic Gaussian Hair](https://mlsanity.com/paper/2512.17094/) (2025)
- [PhysHead: Simulation-Ready Gaussian Head Avatars](https://mlsanity.com/paper/2604.06467/) (2026)
- [EMS: 3D Eyebrow Modeling from Single-view Images](https://mlsanity.com/paper/2309.12787/) (2023)
- [GeomHair: Reconstruction of Hair Strands from Colorless 3D Scans](https://mlsanity.com/paper/2505.05376/) (2025)
- [HairOrbit: Multi-view Aware 3D Hair Modeling from Single Portraits](https://mlsanity.com/paper/2604.02867/) (2026)
- [CGHair: Compact Gaussian Hair Reconstruction with Card Clustering](https://mlsanity.com/paper/2604.03716/) (2026)
- [Unsupervised 3D Braided Hair Reconstruction from a Single-View Image](https://mlsanity.com/paper/2506.23072/) (2025)
- [EfficientMonoHair: Fast Strand-Level Reconstruction from Monocular Video via Multi-View Direction Fusion](https://mlsanity.com/paper/2604.05794/) (2026)

## Abstract (arXiv)

Undoubtedly, high-fidelity 3D hair plays an indispensable role in digital humans. However, existing monocular hair modeling methods are either tricky to deploy in digital systems (e.g., due to their dependence on complex user interactions or large databases) or can produce only a coarse geometry. In this paper, we introduce NeuralHDHair, a flexible, fully automatic system for modeling high-fidelity hair from a single image. The key enablers of our system are two carefully designed neural networks: an IRHairNet (Implicit representation for hair using neural network) for inferring high-fidelity 3D hair geometric features (3D orientation field and 3D occupancy field) hierarchically and a GrowingNet(Growing hair strands using neural network) to efficiently generate 3D hair strands in parallel. Specifically, we perform a coarse-to-fine manner and propose a novel voxel-aligned implicit function (VIFu) to represent the global hair feature, which is further enhanced by the local details extracted from a hair luminance map. To improve the efficiency of a traditional hair growth algorithm, we adopt a local neural implicit function to grow strands based on the estimated 3D hair geometric features. Extensive experiments show that our method is capable of constructing a high-fidelity 3D hair model from a single image, both efficiently and effectively, and achieves the-state-of-the-art performance.