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. See our project page: https://4dqv.mpi-inf.mpg.de/NeuralClothSim/.

翻译：尽管现有的三维布料模拟器能够生成逼真的结果，但它们主要基于具有固定空间分辨率的离散表面表示（如点云和网格）运行，这通常会导致巨大的内存消耗和依赖于分辨率的模拟。此外，通过现有求解器反向传播梯度十分困难，且它们不易集成到现代神经架构中。为此，本文重新思考了物理上合理的布料模拟：我们提出了NeuralClothSim，即一种使用薄壳的新型准静态布料模拟器，其中表面形变以神经场的形式编码在神经网络权重中。我们内存高效的求解器基于一种称为神经形变场（NDFs）的新型连续坐标基表面表示运行；它通过结合非线性各向异性材料模型的非线性Kirchhoff-Love薄壳理论定律来监督NDF的平衡状态。NDFs具有自适应性：它们1）将其容量分配给形变细节，并且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，例如材料插值和模拟编辑。实验结果凸显了我们连续神经公式的有效性。详见项目页面：https://4dqv.mpi-inf.mpg.de/NeuralClothSim/。