# One-shot Compositional 3D Head Avatars with Deformable Hair

arXiv: https://arxiv.org/abs/2604.14782 · HTML: https://mlsanity.com/paper/2604.14782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 49 of its 109 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 30 of 49)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set](https://mlsanity.com/paper/1903.08527/) (2019)
- [Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction](https://mlsanity.com/paper/2012.03065/) (2020)
- [Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields](https://mlsanity.com/paper/2312.03203/) (2023)
- [GaussianAvatars: Photorealistic Head Avatars with Rigged 3D Gaussians](https://mlsanity.com/paper/2312.02069/) (2023)
- [FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction](https://mlsanity.com/paper/2003.13989/) (2020)
- [Towards Metrical Reconstruction of Human Faces](https://mlsanity.com/paper/2204.06607/) (2022)
- [OpenGaussian: Towards Point-Level 3D Gaussian-based Open Vocabulary Understanding](https://mlsanity.com/paper/2406.02058/) (2024)
- [Pixel Codec Avatars](https://mlsanity.com/paper/2104.04638/) (2021)
- [MoFaNeRF: Morphable Facial Neural Radiance Field](https://mlsanity.com/paper/2112.02308/) (2021)
- [Multiface: A Dataset for Neural Face Rendering](https://mlsanity.com/paper/2207.11243/) (2022)
- [SNUG: Self-Supervised Neural Dynamic Garments](https://mlsanity.com/paper/2204.02219/) (2022)
- [HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics](https://mlsanity.com/paper/2212.07242/) (2022)
- [GPAvatar: Generalizable and Precise Head Avatar from Image(s)](https://mlsanity.com/paper/2401.10215/) (2024)
- [OTAvatar: One-shot Talking Face Avatar with Controllable Tri-plane Rendering](https://mlsanity.com/paper/2303.14662/) (2023)
- [Portrait4D-v2: Pseudo Multi-View Data Creates Better 4D Head Synthesizer](https://mlsanity.com/paper/2403.13570/) (2024)
- [NOFA: NeRF-based One-shot Facial Avatar Reconstruction](https://mlsanity.com/paper/2307.03441/) (2023)
- [Avat3r: Large Animatable Gaussian Reconstruction Model for High-fidelity 3D Head Avatars](https://mlsanity.com/paper/2502.20220/) (2025)
- [GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians](https://mlsanity.com/paper/2402.10483/) (2024)
- [RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars](https://mlsanity.com/paper/2305.13353/) (2023)
- [HeadGAP: Few-Shot 3D Head Avatar via Generalizable Gaussian Priors](https://mlsanity.com/paper/2408.06019/) (2024)
- [LAM: Large Avatar Model for One-shot Animatable Gaussian Head](https://mlsanity.com/paper/2502.17796/) (2025)
- [Non-rigid Point Cloud Registration with Neural Deformation Pyramid](https://mlsanity.com/paper/2205.12796/) (2022)
- [Human Hair Reconstruction with Strand-Aligned 3D Gaussians](https://mlsanity.com/paper/2409.14778/) (2024)
- [Stable-Hair: Real-World Hair Transfer via Diffusion Model](https://mlsanity.com/paper/2407.14078/) (2024)
- [VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment](https://mlsanity.com/paper/2312.04651/) (2023)
- [GaussianHeads: End-to-End Learning of Drivable Gaussian Head Avatars from Coarse-to-fine Representations](https://mlsanity.com/paper/2409.11951/) (2024)
- [HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles](https://mlsanity.com/paper/2312.11666/) (2023)

## Abstract (arXiv)

We propose a compositional method for constructing a complete 3D head avatar from a single image. Prior one-shot holistic approaches frequently fail to produce realistic hair dynamics during animation, largely due to inadequate decoupling of hair from the facial region, resulting in entangled geometry and unnatural deformations. Our method explicitly decouples hair from the face, modeling these components using distinct deformation paradigms while integrating them into a unified rendering pipeline. Furthermore, by leveraging image-to-3D lifting techniques, we preserve fine-grained textures from the input image to the greatest extent possible, effectively mitigating the common issue of high-frequency information loss in generalized models. Specifically, given a frontal portrait image, we first perform hair removal to obtain a bald image. Both the original image and the bald image are then lifted to dense, detail-rich 3D Gaussian Splatting (3DGS) representations. For the bald 3DGS, we rig it to a FLAME mesh via non-rigid registration with a prior model, enabling natural deformation that follows the mesh triangles during animation. For the hair component, we employ semantic label supervision combined with a boundary-aware reassignment strategy to extract a clean and isolated set of hair Gaussians. To control hair deformation, we introduce a cage structure that supports Position-Based Dynamics (PBD) simulation, allowing realistic and physically plausible transformations of the hair Gaussian primitives under head motion, gravity, and inertial effects. Striking qualitative results, including dynamic animations under diverse head motions, gravity effects, and expressions, showcase substantially more realistic hair behavior alongside faithfully preserved facial details, outperforming state-of-the-art one-shot methods in perceptual realism.