We propose a novel Augmented Mass-Spring (AMS) model for real-time simulation of dense hair at strand level. Our approach considers the traditional edge, bending, and torsional degrees of freedom in mass-spring systems, but incorporates an additional one-way biphasic coupling with a ghost rest-shape configuration. Trough multiple evaluation experiments with varied dynamical settings, we show that AMS improves the stability of the simulation in comparison to mass-spring discretizations, preserves global features, and enables the simulation of non-Hookean effects. Using an heptadiagonal decomposition of the resulting matrix, our approach provides the efficiency advantages of mass-spring systems over more complex constitutive hair models, while enabling a more robust simulation of multiple strand configurations. Finally, our results demonstrate that our framework enables the generation, complex interactivity, and editing of simulation-ready dense hair assets in real-time. More details can be found on our project page: https://agrosamad.github.io/AMS/.

翻译：我们提出了一种新颖的增强型质点弹簧模型，用于在发丝级别实时模拟密集头发。我们的方法考虑了质点弹簧系统中传统的边、弯曲和扭转自由度，但引入了与幽灵静止形态配置的单向双相耦合。通过在不同动力学设置下的多项评估实验，我们证明相较于质点弹簧离散化方法，增强型质点弹簧模型提升了模拟稳定性，保持了全局特征，并能模拟非胡克效应。通过对所得矩阵进行七对角分解，我们的方法在保持比复杂本构头发模型更高计算效率的同时，实现了对多股发丝配置更稳健的模拟。最终结果表明，我们的框架能够实时生成具有复杂交互性且可直接用于模拟的密集头发资产，并支持实时编辑。更多细节详见项目页面：https://agrosamad.github.io/AMS/。