PhysDrape: Learning Explicit Forces and Collision Constraints for Physically Realistic Garment Draping

Garment draping aims to fit clothing onto 3D human mod-els. Existing physics-based methods are computationally expensive andstruggle to integrate with general differentiable systems. In contrast, deeplearning-based methods often require extensive annotations and lack ex-plicit physical constraints, limiting their ability to model accurate details.We innovate by bridging physical models with deep learning and proposePhysDrape. It integrates three modules using forces as an intermediary:the force-driven GNN predicts forces at each node, the stretching solvermodels physical deformation, and the collision handler penalizes inter-penetration. Each module includes learnable parameters to fit underly-ing force propagation processes and physical properties, enabling gener-alization to unseen templates and control over properties like stiffness.PhysDrape enables end-to-end optimization, leveraging intrinsic garmentenergy for physically plausible deformation via self-supervised learning.Following the previous training and evaluation protocol on CLOTH3Ddataset, PhysDrape achieves lower energy scores, negligible interpene-tration, more realistic visualizations, and comparable time consumption.