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.

基于神经网络权重的新的连续坐标表达动态表面的布料模拟方法 NeuralClothSim 在现有的有限元模拟方法的基础上提出了一种新的解决方案，通过神经网络权重编码表面演化，并使用 Kirchhoff-Love 壳理论的规则进行 NDF 演化的监督，解决了现有布料模拟方法的空间、时间和存储限制等问题，同时展示了材料插值和模拟编辑等多个应用，实验证明了该方法的有效性和潜在影响。