Cloth simulation is an extensively studied problem, with a plethora of solutions available in computer graphics literature. Existing cloth simulators produce realistic cloth deformations that obey different types of boundary conditions. Nevertheless, their operational principle remains limited in several ways: They operate on explicit surface representations with a fixed spatial resolution, perform a series of discretised updates (which bounds their temporal resolution), and require comparably large amounts of storage. Moreover, back-propagating gradients through the existing solvers is often not straightforward, which poses additional challenges when integrating them into modern neural architectures. In response to the limitations mentioned above, this paper takes a fundamentally different perspective on physically-plausible cloth simulation and re-thinks this long-standing problem: We propose NeuralClothSim, i.e., a new cloth simulation approach using thin shells, in which surface evolution is encoded in neural network weights. Our memory-efficient and differentiable solver operates on a new continuous coordinate-based representation of dynamic surfaces, i.e., neural deformation fields (NDFs); it supervises NDF evolution with the rules of the non-linear Kirchhoff-Love shell theory. NDFs are adaptive in the sense that they 1) allocate their capacity to the deformation details as the latter arise during the cloth evolution and 2) allow surface state queries at arbitrary spatial and temporal resolutions without retraining. We show how to train our NeuralClothSim solver while imposing hard boundary conditions and demonstrate multiple applications, such as material interpolation and simulation editing. The experimental results highlight the effectiveness of our formulation and its potential impact.

翻译：布料模拟是一个被广泛研究的问题，在计算机图形学文献中有大量解决方案。现有的布料模拟器能生成符合不同类型边界条件的逼真布料形变。然而，其运行原理在多个方面仍存在局限性：它们在固定空间分辨率的显式表面表示上运行，执行一系列离散化更新（这限制了时间分辨率），且需要相对较大的存储空间。此外，通过现有求解器反向传播梯度通常并不直接，这为将其集成到现代神经架构中带来了额外挑战。针对上述局限性，本文从根本性的不同视角审视物理合理的布料模拟，并重新思考这一长期存在的问题：我们提出了NeuralClothSim，即一种使用薄壳的新布料模拟方法，其中表面演化被编码在神经网络权重中。我们提出的内存高效且可微的求解器基于动态表面的新型连续坐标表示——即神经形变场（NDFs）；我们利用非线性基尔霍夫-洛夫壳理论规则来监督NDF的演化。NDF具有自适应性，表现在：1）随着布料演化过程中形变细节的出现，它们能将自身容量分配到这些细节上；2）允许在任意空间和时间分辨率下查询表面状态而无需重新训练。我们展示了如何在施加硬边界条件的情况下训练NeuralClothSim求解器，并演示了多种应用，例如材料插值和模拟编辑。实验结果突显了我们方法的有效性及其潜在影响。