Recent advances in digital avatar technology have enabled the generation of compelling virtual characters, but deploying these avatars on compute-constrained devices poses significant challenges for achieving realistic garment deformations. While physics-based simulations yield accurate results, they are computationally prohibitive for real-time applications. Conversely, linear blend skinning offers efficiency but fails to capture the complex dynamics of loose-fitting garments, resulting in unrealistic motion and visual artifacts. Neural methods have shown promise, yet they struggle to animate loose clothing plausibly under strict performance constraints. In this work, we present a novel approach for fast and physically plausible garment draping tailored for resource-constrained environments. Our method leverages a reduced-space quasi-static neural simulation, mapping the garment's full degrees of freedom to a set of bone handles that drive deformation. A neural deformation model is trained in a fully self-supervised manner, eliminating the need for costly simulation data. At runtime, a lightweight neural network modulates the handle deformations based on body shape and pose, enabling realistic garment behavior that respects physical properties such as gravity, fabric stretching, bending, and collision avoidance. Experimental results demonstrate that our method achieves physically plausible garment drapes while generalizing across diverse poses and body shapes, supporting zero-shot evaluation and mesh topology independence. Our method's runtime significantly outperforms past works, as it runs in microseconds per frame using single-threaded CPU inference, offering a practical solution for real-time avatar animation on low-compute devices.

翻译：数字虚拟人技术的近期进展使得生成引人入胜的虚拟角色成为可能，但在计算资源受限设备上部署这些角色时，实现逼真的服装形变仍面临重大挑战。基于物理的仿真虽能产生精确结果，但其计算开销过大，难以满足实时应用需求。相比之下，线性混合蒙皮方法虽具有高效性，却无法捕捉宽松服装的复杂动力学特性，导致运动失真和视觉伪影。神经方法虽展现出潜力，但在严格性能约束下难以合理模拟宽松衣物的动态。本文提出一种面向资源受限环境的新型快速物理合理服装悬垂方法。该方法利用降阶准静态神经仿真，将服装的全自由度映射至一组驱动形变的骨骼控制点。神经形变模型通过完全自监督方式训练，无需昂贵的仿真数据。运行时，轻量级神经网络根据体型和姿态调整控制点形变，实现兼顾重力、织物拉伸、弯曲及碰撞避免等物理属性的逼真服装行为。实验结果表明，本方法能生成物理合理的服装悬垂效果，同时支持跨姿态与体型的泛化，具备零样本评估和网格拓扑无关特性。其运行时性能显著优于既往工作，通过单线程CPU推理即可实现每帧微秒级处理，为低算力设备上的实时虚拟人动画提供了实用解决方案。