Despite existing 3D cloth simulators producing realistic results, they predominantly operate on discrete surface representations (e.g. points and meshes) with a fixed spatial resolution, which often leads to large memory consumption and resolution-dependent simulations. Moreover, back-propagating gradients through the existing solvers is difficult, and they cannot be easily integrated into modern neural architectures. In response, this paper re-thinks physically plausible cloth simulation: We propose NeuralClothSim, i.e., a new quasistatic cloth simulator using thin shells, in which surface deformation is encoded in neural network weights in the form of a neural field. Our memory-efficient solver operates on a new continuous coordinate-based surface representation called neural deformation fields (NDFs); it supervises NDF equilibria with the laws of the non-linear Kirchhoff-Love shell theory with a non-linear anisotropic material model. NDFs are adaptive: They 1) allocate their capacity to the deformation details and 2) allow surface state queries at arbitrary spatial resolutions without re-training. We show how to train NeuralClothSim while imposing hard boundary conditions and demonstrate multiple applications, such as material interpolation and simulation editing. The experimental results highlight the effectiveness of our continuous neural formulation.

翻译：尽管现有的三维织物模拟器能够产生逼真的结果，但它们主要基于离散的表面表示（如点云和网格）并以固定的空间分辨率运行，这通常导致较大的内存消耗和分辨率依赖的模拟。此外，通过现有求解器反向传播梯度较为困难，且它们不易与现代神经架构集成。为此，本文重新思考了物理上合理的织物模拟：我们提出了NeuralClothSim，即一种使用薄壳的新型准静态织物模拟器，其中表面变形以神经场的形式编码在神经网络权重中。我们高效内存的求解器基于一种称为神经变形场（NDFs）的新型连续坐标表面表示运行；它通过结合非线性各向异性材料模型的非线性Kirchhoff-Love壳理论定律来监督NDF的平衡状态。NDFs具有自适应性：它们1）将其容量分配给变形细节，且2）允许在任意空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同时训练NeuralClothSim，并演示了多种应用，如材料插值和模拟编辑。实验结果突显了我们连续神经公式的有效性。