Augmented Mass-Spring Model for Real-Time Dense Hair Simulation

J. Alejandro Amador H., Yi Zhou, Xin Sun, Zhixin Shu, Chengan He, Soren Pirk, Dominik L. Michels; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 11339-11347

We propose a novel Augmented Mass-Spring (AMS) model for real-time simulation of dense hair at the 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. Through 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 a 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.