# FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets

arXiv: https://arxiv.org/abs/2609.35770 · HTML: https://mlsanity.com/paper/2609.35770/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 18 references that are in the map.
- First posted on arXiv 28 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction](https://mlsanity.com/paper/2212.05231/) (2022)
- [PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond](https://mlsanity.com/paper/2504.11451/) (2025)
- [GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians](https://mlsanity.com/paper/2402.10483/) (2024)
- [Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape](https://mlsanity.com/paper/2308.11737/) (2023)
- [Neural Haircut: Prior-Guided Strand-Based Hair Reconstruction](https://mlsanity.com/paper/2306.05872/) (2023)
- [Human Hair Reconstruction with Strand-Aligned 3D Gaussians](https://mlsanity.com/paper/2409.14778/) (2024)
- [GroomGen: A High-Quality Generative Hair Model Using Hierarchical Latent Representations](https://mlsanity.com/paper/2311.02062/) (2023)
- [Perm: A Parametric Representation for Multi-Style 3D Hair Modeling](https://mlsanity.com/paper/2407.19451/) (2024)
- [AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer](https://mlsanity.com/paper/2412.00837/) (2024)
- [Gaussian Frosting: Editable Complex Radiance Fields with Real-Time Rendering](https://mlsanity.com/paper/2403.14554/) (2024)
- [Generative Zoo](https://mlsanity.com/paper/2412.08101/) (2024)
- [NeuralFur: Animal Fur Reconstruction From Multi-View Images](https://mlsanity.com/paper/2601.12481/) (2026)
- [Name That Part: 3D Part Segmentation and Naming](https://mlsanity.com/paper/2512.18003/) (2025)
- [CGHair: Compact Gaussian Hair Reconstruction with Card Clustering](https://mlsanity.com/paper/2604.03716/) (2026)

## Abstract (arXiv)

Realistic and editable animal fur reconstruction from multi-view images is challenging due to fine-scale detail, self-occlusion and obfuscation, and, unlike human hair, the lack of animal-fur datasets. Fur usually covers most of an animal's body, with large inter-species and intra-species variability. We present FurE, an efficient strand-based animal fur reconstruction method that recovers a per-strand, editable groom by optimizing a root-conditioned latent field, decoded into strand geometry via a PCA-based decoder. We reconstruct a defurred animal body using local fur-thickness cues from a surface-constrained Gaussian Frosting representation together with part-based priors. We further show that a PCA-based decoder learned from human-hair strand data can alleviate animal-data scarcity while enabling substantially faster optimization. FurE achieves a 10x speedup in strand training over current SOTA dense per-strand optimization while retaining strand fidelity and generalizing across synthetic and real-world sequences, with quantitative and qualitative validation despite the reduction in training time.