FurE：无需动物毛发数据集的高效实例特定三维毛发重建
FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets
- Johns Hopkins University（约翰霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
FurE提出一种基于发丝的高效动物毛发重建方法，利用PCA解码器和根条件潜在场，无需动物毛发数据集，实现10倍训练加速并保持保真度。
AI中文摘要：
从多视角图像进行逼真且可编辑的动物毛发重建具有挑战性，原因在于精细的细节、自遮挡和遮蔽，以及与人发不同，缺乏动物毛发数据集。毛发通常覆盖动物身体的大部分区域，且存在物种间和物种内的巨大差异。我们提出了FurE，一种高效的基于发丝的动物毛发重建方法，通过优化根条件潜在场来恢复每根发丝的可编辑梳理结果，并借助基于PCA的解码器将潜在场解码为发丝几何。我们利用表面约束的高斯霜状表示中的局部毛发厚度线索，结合基于部件的先验，重建去除毛发的动物身体。我们进一步证明，从人发发丝数据学习的PCA解码器可以缓解动物数据稀缺问题，同时实现显著更快的优化。FurE在发丝训练上相比当前最先进的密集逐发丝优化实现了10倍加速，同时保持发丝保真度，并泛化到合成和真实世界序列，尽管训练时间减少，但定量和定性验证均表现良好。
英文摘要：
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.
