Physics-based simulation of mesh based domains remains a challenging task. State-of-the-art techniques can produce realistic results but require expert knowledge. A major bottleneck in many approaches is the step of integrating a potential energy in order to compute velocities or displacements. Recently, learning based method for physics-based simulation have sparked interest with graph based approaches being a promising research direction. One of the challenges for these methods is to generate models that are mesh independent and generalize to different material properties. Moreover, the model should also be able to react to unforeseen external forces like ubiquitous collisions. Our contribution is based on a simple observation: evaluating forces is computationally relatively cheap for traditional simulation methods and can be computed in parallel in contrast to their integration. If we learn how a system reacts to forces in general, irrespective of their origin, we can learn an integrator that can predict state changes due to the total forces with high generalization power. We effectively factor out the physical model behind resulting forces by relying on an opaque force module. We demonstrate that this idea leads to a learnable module that can be trained on basic internal forces of small mesh patches and generalizes to different mesh typologies, resolutions, material parameters and unseen forces like collisions at inference time. Our proposed paradigm is general and can be used to model a variety of physical phenomena. We focus our exposition on the detail enhancement of coarse clothing geometry which has many applications including computer games, virtual reality and virtual try-on.

翻译：摘要：基于网格域的物理仿真仍然是一项具有挑战性的任务。当前最先进的技术可以产生逼真的结果，但需要专业知识。许多方法中的一个主要瓶颈是结合势能以计算速度或位移的步骤。近年来，基于学习的物理仿真方法引起了广泛兴趣，其中基于图的方法成为一个有前景的研究方向。这些方法面临的挑战之一是生成与网格无关并能泛化到不同材料属性的模型。此外，模型还应能够应对不可预见的外力（如普遍存在的碰撞）。我们的贡献基于一个简单观察：对于传统仿真方法，计算力的计算成本相对较低，且可并行计算，而积分步骤则不然。如果我们学习系统如何普遍应对各种来源的力，我们就可以学习一个积分器，该积分器能基于总力预测状态变化，具有高度泛化能力。通过依赖一个不透明的力模块，我们有效分离了产生力的物理模型。我们证明，这一思路可构建一个可学习模块，该模块可基于小网格补片的基本内力进行训练，并在推理时泛化到不同网格拓扑、分辨率、材料参数以及不可预见的外力（如碰撞）。我们提出的范式具有通用性，可用于建模多种物理现象。我们重点展示了其在粗布料几何细节增强中的应用，该应用在计算机游戏、虚拟现实和虚拟试穿等领域具有广泛用途。