3D face reconstruction methods exhibit significant limitations when applied to pathological cases such as facial paralysis, due to inherent challenges including asymmetric motion and non-linear muscle dynamics. To address these gaps, we propose SFPFR, a self-supervised framework for facial paralysis 3D face reconstruction leveraging 1-3 viewpoints. We first propose a self-supervised learning paradigm integrating reconstruction loss, multi-view consistency loss, and a Mamba-based temporal loss to reconstruct 3D face without ground-truth; then, a partitioned dynamic fusion module that adaptively weights multi-view features ensuring precise geometric reconstruction and pathological detail preservation; last, we propose FPD-100, the first multi-view video dataset for facial paralysis, comprising 30,000 frames from 100 patients of 3 views. Extensive experiments validate SFPFR’s superiority, achieving state-of-the-art PSNR (27.74) and FID (37.13). It enables clinical applications in severity assessment, rehabilitation monitoring, and treatment planning, while the dataset and code will be open-sourced to catalyze research in pathological facial analysis.