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arXiv Computer Vision By Fenggui Rao, Yan Luximon, Jie Zhang

DisFace3DNet: Explainable Facial Attractiveness Prediction via 3D Component Disentanglement

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DisFace3DNet is a facial attractiveness prediction model that disentangles 3D facial components—identity, skin, hair, light, background, expression, and pose—to learn seven component reference scores from overall ratings using weak semantic supervision. The model combines five static and two signed dynamic scores to produce an overall rating, revealing each component’s numerical contribution and enabling component‑specific comparisons across images. On the SCUT‑FBP5500 dataset, it achieves a Pearson correlation of 0.8904±0.0063 with human ratings, with skin, hair, and facial shape contributing most to prediction variation.

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