PhysDrape: 基于显式力和碰撞约束的物理真实服装褶皱学习
PhysDrape: Learning Explicit Forces and Collision Constraints for Physically Realistic Garment Draping
AI总结：
PhysDrape通过显式力和碰撞约束，实现物理真实的服装褶皱学习，提升仿真精度和鲁棒性。
摘要：
深度学习驱动的服装褶皱技术已成为传统物理模拟（PBS）的有前景替代方案，但稳健的碰撞处理仍然是关键瓶颈。现有大多数方法通过软惩罚强制物理有效性，造成几何可行性与物理合理性之间的内在权衡：惩罚碰撞常扭曲网格结构，而保持形状则导致相互穿透。为解决这一矛盾，我们提出了PhysDrape，一种混合神经-物理求解器，用于物理真实的服装褶皱，由显式力和约束驱动。不同于软约束框架，PhysDrape在完全可微的管道中整合神经推理与显式几何求解器。具体而言，我们提出了一种受物理增强图影响的图神经网络，编码材料参数和身体接近度，以预测残差位移。关键在于整合一个可微的两阶段求解器：首先，一个可学习的力求解器迭代解决从圣文森特- Kirchhoff（StVK）模型导出的不平衡力，以确保准静态平衡；其次，一个可微的投影严格强制与身体表面的碰撞约束。这种可微设计通过显式约束保证物理有效性，同时使端到端学习能够优化网络以获得物理一致的预测。大量实验表明，PhysDrape实现了最先进的性能，确保与现有基线相比显著更低的形变能量，同时在实时中实现了更优的物理真实性和鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
