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Recent neural, physics-based modeling of garment deformations allows fasterand visually aesthetic results as opposed to the existing methods.Material-specific parameters are used by the formulation to control the garmentinextensibility. This delivers unrealistic results with physically implausiblestretching. Oftentimes, the draped garment is pushed inside the body which iseither corrected by an expensive post-processing, thus adding to furtherinconsistent stretching; or by deploying a separate training regime for eachbody type, restricting its scalability. Additionally, the flawed skinningprocess deployed by existing methods produces incorrect results on loosegarments.  In this paper, we introduce a geometrical constraint to the existingformulation that is collision-aware and imposes garment inextensibilitywherever possible. Thus, we obtain realistic results where draped clothesstretch only while covering bigger body regions. Furthermore, we propose ageometry-aware garment skinning method by defining a body-garment closenessmeasure which works for all garment types, especially the loose ones.

GAPS: Geometry-Aware, Physics-Based, Self-Supervised Neural Garment Draping