For decades, real-time skinning has been the cornerstone of character animation in visual effects and games. Despite its importance, the creation of animatable digital assets remains a labor-intensive manual process. Existing automated tools frequently struggle with intricate geometries, often necessitating significant manual refinement to reach production standards. We present a robust, fully automated method for generating high-quality skinning weights from a standard mesh and skeleton in a canonical A- or T-pose. Unlike traditional approaches, our framework offers direct sparsity controls to limit bone influences per vertex -- a critical requirement for maintaining performance in large-scale mobile environments. Furthermore, we address the challenge of Level-of-Detail (LoD) management by optimizing weights within a continuous spatial volume rather than on discrete vertices. This allows a single optimization pass to be applied seamlessly across multiple asset resolutions and variations. Central to our approach is a novel parameterized family of functions, we call SkinCells. We demonstrate that our method consistently produces stable, high-quality results even in complex scenarios where standard biharmonic weight computations fail.

翻译：数十年来，实时蒙皮技术一直是视觉特效和游戏角色动画的基石。尽管其重要性不言而喻，可动画数字资产的创建至今仍是劳动密集的手动流程。现有自动化工具常难以处理复杂几何结构，往往需要大量人工调整才能达到生产标准。本文提出一种鲁棒的全自动方法，能够从标准网格和规范A/T姿态骨架生成高质量的蒙皮权重。与传统方法不同，我们的框架提供直接的稀疏性控制以限制每个顶点所受骨骼影响数——这是在大规模移动端环境中维持性能的关键需求。此外，我们通过将权重优化置于连续空间体而非离散顶点上，解决了细节层次管理难题。这使得单次优化过程可无缝应用于多种资产分辨率和变体。我们方法的核心是称为SkinCells的新型参数化函数族。实验表明，即使在标准双调和权重计算失效的复杂场景中，我们的方法仍能持续生成稳定、高质量的结果。