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

Realistic and editable animal fur reconstruction from multi-view images is challenging - fine-scale detail, self-occlusion and obfuscation, and, unlike human hair, there are no animal fur datasets, and fur usually covers most of the animal's body - with large inter/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 surface constrained gaussian "frosting" representation, along with part-based priors. We next show using a PCA-based decoder using knowledge from human hair strand data - allows us to alleviate the animal data scarcity, while allowing for faster optimization. FurE achieves a 10$\times$ strand training speedup over current SOTA dense per-strand optimization, retaining strand fidelity and generalizing across synthetic and more importantly, real-world sequences, with quantitative and quantitative validation despite this reduction in training time.