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.

通过引入几何约束和基于物理的模型，本文解决了现有方法中的不逼真的问题，并提出了适用于所有服装类型的几何感知的服装皮肤方法。