The fundamental limitation of traditional strand-based modeling is not simply data scarcity, but the ill-posedness of inferring complex 3D fields from 2D imagery without structural constraints. This unconstrained regression leads to catastrophic failures in resolving both global occlusion (e.g., in ponytails) and local directionality (e.g., in curls), resulting in over-smoothed, plausible-but-incorrect geometries. To resolve this, we integrate the strong geometric priors of Large Reconstruction Models (LRMs) into the strand generation pipeline. Using the LRM mesh as a structural anchor, we employ a novel Dual Orientation AutoEncoder to lift coarse geometry into high-fidelity strands. By resolving vector field singularities through latent-space optimization and surface-guided refinement, our method effectively disentangles complex topological structures, setting a new benchmark for robustness and accuracy in hair reconstruction.

翻译：传统基于发丝建模的根本局限性不仅在于数据稀缺，更在于缺乏结构约束时从二维图像推断复杂三维场的不适定性。这种无约束回归会导致在解决全局遮挡（如马尾辫）和局部方向性（如卷发）时出现灾难性失败，产生过度平滑的看似合理但实际错误的几何结构。为解决这一问题，我们将大规模重建模型（LRM）的强几何先验融入发丝生成流程。以LRM网格作为结构锚点，我们采用新颖的双向方向自编码器将粗略几何提升为高保真发丝。通过潜空间优化和表面引导细化来解决矢量场奇异性，该方法有效解耦了复杂拓扑结构，为毛发重建的鲁棒性和准确性树立了新标杆。