发表机构
Penn State University; Roblox（宾夕法尼亚州立大学; Roblox公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出切割单元蒙皮先验，将其集成到神经蒙皮模型中，实现了比优化求解器快数量级的速度与拓扑鲁棒性，提升了现有方法性能并达到最先进水平。
AI 中文摘要
我们提出了切割单元蒙皮（cut-cell skinning），一种旨在增强数据驱动蒙皮权重生成的几何先验。尽管数据驱动方法在生成高质量蒙皮权重方面展现出潜力，但它们往往缺乏经典几何方法的泛化能力。为弥合这一差距，我们提出了一种可针对野外网格（in-the-wild meshes）稳健计算且适用于大规模机器学习工作流的几何先验。切割单元蒙皮的核心思想是基于图的体积测地线距离快速近似，其灵感来源于体积测地线距离在经典蒙皮权重计算中的重要性。与基于优化的求解器相比，我们的方法实现了数量级的加速，且对基于 cage 或体素的替代方法中常见的拓扑伪影具有鲁棒性。我们通过将切割单元蒙皮先验集成到近期的神经蒙皮模型中，验证了其有效性，结果显示其在现有方法中实现了一致的性能提升并达到了最先进（state-of-the-art）的水平。项目页面：this https URL
英文摘要
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/
