The accurate representation of fine-detailed cloth wrinkles poses significant challenges in computer graphics. The inherently non-uniform structure of cloth wrinkles mandates the employment of intricate discretization strategies, which are frequently characterized by high computational demands and complex methodologies. Addressing this, the research introduced in this paper elucidates a novel anisotropic cloth regression technique that capitalizes on the potential of implicit neural representations of surfaces. Our first core contribution is an innovative mesh-free sampling approach, crafted to reduce the reliance on traditional mesh structures, thereby offering greater flexibility and accuracy in capturing fine cloth details. Our second contribution is a novel adversarial training scheme, which is designed meticulously to strike a harmonious balance between the sampling and simulation objectives. The adversarial approach ensures that the wrinkles are represented with high fidelity, while also maintaining computational efficiency. Our results showcase through various cloth-object interaction scenarios that our method, given the same memory constraints, consistently surpasses traditional discrete representations, particularly when modelling highly-detailed localized wrinkles.

翻译：精细布料皱纹的精确表示在计算机图形学中面临重大挑战。布料皱纹固有的非均匀结构要求采用复杂的离散化策略，这类策略通常具有高计算需求且方法复杂。针对这一问题，本文提出的研究阐述了一种新颖的各向异性布料回归技术，该技术充分利用了曲面隐式神经表示的潜力。我们的第一个核心贡献是一种创新的无网格采样方法，旨在降低对传统网格结构的依赖，从而在捕捉精细布料细节方面提供更高的灵活性和精度。第二个贡献是一种新颖的对抗训练方案，该方案经过精心设计，可在采样与模拟目标之间实现和谐平衡。对抗方法确保皱纹以高保真度呈现，同时保持计算效率。我们的结果通过多种布料-物体交互场景展示，在相同内存约束条件下，该方法始终优于传统离散表示，尤其是对高度细节化的局部皱纹进行建模时。