[
    {
        "id": "osp-20890",
        "type": "article-journal",
        "title": "FurE: Efficient Instance-Specific 3D Fur Reconstruction without Animal-Fur Datasets",
        "author": [
            {
                "family": "Sarkar",
                "given": "Srinjay"
            },
            {
                "family": "Kaushik",
                "given": "Prakhar"
            },
            {
                "family": "Paul",
                "given": "Soumava"
            },
            {
                "family": "Yuille",
                "given": "Alan"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/fure-efficient-instance-specific-3d-fur-reconstruction-without-animal-fur-datasets",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "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."
    }
]