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：近期基于物理的神经服装形变建模方法相较于现有技术实现了更快速且视觉上更美观的结果。现有公式通过材料特定参数控制服装的不可拉伸性，但这会导致物理上不合理的拉伸伪影。同时，悬垂服装常被推入人体内部，要么通过昂贵的后处理进行修正（进一步加剧不一致拉伸），要么为每种体型部署独立训练机制（限制可扩展性）。此外，现有方法中 flawed 的蒙皮过程对宽松服装会产生错误结果。本文在现有公式中引入几何约束，该约束具有碰撞感知能力，并在尽可能的条件下施加服装不可拉伸性，从而在覆盖较大身体区域时仅产生合理拉伸的真实结果。进一步，我们提出一种几何感知的服装蒙皮方法，通过定义身体-服装接近度度量，该方法适用于所有服装类型，尤其针对宽松服装。