用大模型先验重建头发结构，解决2D图像转3D易出错的问题。
HairLRM: Strand-based Hair Modeling via Large Reconstruction Models
- 用大重建模型的几何先验作为骨架，指导头发丝生成。
- 在复杂发髻和卷曲处保持准确方向性，避免过度平滑。
- 适合需要高精度头发建模的影视动画与虚拟人场景。
传统基于发丝的建模问题不仅源于数据稀缺，更在于从2D图像推断复杂3D场时缺乏结构约束，导致无约束回归在全局遮挡（如马尾）和局部方向性（如卷发）上失效，产生看似合理却错误的几何结构。为此，我们引入大型重建模型（LRM）的强几何先验，嵌入发丝生成流程。以LRM网格为结构锚点，采用新型双方向自编码器将粗略几何提升为高保真发丝。通过潜在空间优化消除矢量场奇点，并结合表面引导细化，有效解耦复杂拓扑结构，显著提升头发重建的鲁棒性与准确性，树立新基准。
原文摘要 · Abstract (English)
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.
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