🤖 AI Summary
This work addresses the limitations of existing deep learning approaches for garment draping simulation, which rely on soft constraints to handle collisions and often fail to simultaneously ensure geometric feasibility and physical plausibility, leading to mesh distortion or body penetration. To overcome these issues, we propose PhysDrape, a hybrid framework that integrates a physics-informed graph neural network with a differentiable explicit physical solver. Our method introduces, for the first time, a two-stage differentiable solving pipeline—combining force equilibrium optimization with hard projection constraints—and incorporates the Saint Venant–Kirchhoff material model to strictly enforce non-penetration and quasi-static equilibrium during end-to-end training. Experiments demonstrate that PhysDrape significantly reduces strain energy, nearly eliminates body penetration, and achieves superior physical fidelity and real-time robustness compared to current state-of-the-art methods.
📝 Abstract
Deep learning-based garment draping has emerged as a promising alternative to traditional Physics-Based Simulation (PBS), yet robust collision handling remains a critical bottleneck. Most existing methods enforce physical validity through soft penalties, creating an intrinsic trade-off between geometric feasibility and physical plausibility: penalizing collisions often distorts mesh structure, while preserving shape leads to interpenetration. To resolve this conflict, we present PhysDrape, a hybrid neural-physical solver for physically realistic garment draping driven by explicit forces and constraints. Unlike soft-constrained frameworks, PhysDrape integrates neural inference with explicit geometric solvers in a fully differentiable pipeline. Specifically, we propose a Physics-Informed Graph Neural Network conditioned on a physics-enriched graph -- encoding material parameters and body proximity -- to predict residual displacements. Crucially, we integrate a differentiable two-stage solver: first, a learnable Force Solver iteratively resolves unbalanced forces derived from the Saint Venant-Kirchhoff (StVK) model to ensure quasi-static equilibrium; second, a Differentiable Projection strictly enforces collision constraints against the body surface. This differentiable design guarantees physical validity through explicit constraints, while enabling end-to-end learning to optimize the network for physically consistent predictions. Extensive experiments demonstrate that PhysDrape achieves state-of-the-art performance, ensuring negligible interpenetration with significantly lower strain energy compared to existing baselines, achieving superior physical fidelity and robustness in real-time.