HairLRM: Strand-based Hair Modeling via Large Reconstruction Models
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 r...
| Authors | Yuefan Shen, Yican Dong, Xiufeng Huang et al. |
|---|---|
| Primary category | cs.GR |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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