用几何路径距离加速神经皮肤权重生成，提升精度与泛化能力。
A Geodesic Cut-Cell Prior for Neural Skinning
- 基于图算法快速计算体积测地距离，构建几何先验
- 比传统优化方法快多个数量级，且抗拓扑噪声
- 可嵌入神经皮肤模型，适合真实场景的3D角色建模
我们提出一种名为剪切单元皮肤（cut-cell skinning）的几何先验，用于增强数据驱动的皮肤权重生成。尽管数据驱动方法在生成高质量皮肤权重方面展现出潜力，但其泛化能力通常弱于经典几何方法。为弥合这一差距，我们设计了一种可在真实世界网格上稳健计算、并适用于大规模机器学习流程的几何先验。该方法的核心思想是采用快速的图论近似来计算体积测地距离，这在经典皮肤权重计算中具有重要意义。相比基于优化的求解器，本方法实现了数量级的速度提升，并对笼子或体素方法常见的拓扑伪影具有鲁棒性。我们将该先验集成到最新的神经皮肤模型中，结果表明其在现有方法上均带来一致性能提升，达到当前最优水平。
原文摘要 · Abstract (English)
We introduce cut-cell skinning, a geometric prior designed to augment data-driven skinning weight generation. While data-driven methods show promise in producing high-quality skinning weights, they often lack the generalizability of classic geometric approaches. To bridge this gap, we propose a geometric prior that can be robustly computed for in-the-wild meshes and is efficient for large-scale machine learning workflows. The key idea of our cut-cell skinning is a fast graph-based approximation of the volumetric geodesics distances, motivated by their importance in classic skinning weight computation. Our method achieves orders of magnitude speedup compared to optimization-based solvers and remains resilient to topological artifacts common in cage- or voxel-based alternatives. We demonstrate the efficacy of the cut-cell skinning prior by integrating it into recent neural skinning models, showing consistent improvements across existing methods and achieving state-of-the-art results. Project page: https://wenchao-m.github.io/CutCell.github.io/
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