Recent neural, physics-based modeling of garment deformations allows faster and visually aesthetic results as opposed to the existing methods. Material-specific parameters are used by the formulation to control the garment inextensibility. This delivers unrealistic results with physically implausible stretching. Oftentimes, the draped garment is pushed inside the body which is either corrected by an expensive post-processing, thus adding to further inconsistent stretching; or by deploying a separate training regime for each body type, restricting its scalability. Additionally, the flawed skinning process deployed by existing methods produces incorrect results on loose garments. In this paper, we introduce a geometrical constraint to the existing formulation that is collision-aware and imposes garment inextensibility wherever possible. Thus, we obtain realistic results where draped clothes stretch only while covering bigger body regions. Furthermore, we propose a geometry-aware garment skinning method by defining a body-garment closeness measure which works for all garment types, especially the loose ones.

翻译：近年提出的基于物理的神经服装形变建模方法相较于传统方法，能够实现更快速且视觉上更美观的效果。现有公式通过材料特定参数控制服装的不可拉伸性，但会导致不真实的物理拉伸结果。常见问题包括：悬垂服装被穿透至人体内部（需通过昂贵的后处理修正，进而引发更多不连续的拉伸），或为每种体型单独训练模型（限制了可扩展性）。此外，现有方法采用的蒙皮处理对宽松服装会产生错误结果。本文针对现有公式引入几何约束，该约束具有碰撞感知特性，并在可能范围内强制实现服装不可拉伸性，从而获得悬垂服装仅在包裹较大身体区域时产生拉伸的真实效果。同时，我们通过定义身体-服装接近度度量，提出一种适用于所有服装类型（特别是宽松服装）的几何感知蒙皮方法。