FurE：无需动物毛发数据集的高效 3D 动物毛发重建
FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets
论文作者提出 FurE，几张多视角照片就能重建可编辑的动物毛发，靠人类头发数据训练的 PCA 解码器绕开动物数据集，训练还快 10 倍。
FurE 是一种从多视角图像重建动物毛发的 strand 级方法，通过优化 root 条件化的隐场并经 PCA 解码器生成每根毛发的几何。它用 surface-constrained Gaussian Frosting 表示结合局部毛发厚度线索去除动物表皮毛发，并利用基于人类毛发数据训练的 PCA 解码器缓解动物数据稀缺问题。相比当前 SOTA 的逐根优化方法，FurE 在 strand 训练上实现 10 倍加速，同时保持毛发保真度，并在合成与真实序列上完成定量和定性验证。
FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets
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.