The diverse interactions between hair and liquid are complex and span multiple length scales, yet are central to the appearance of humans and animals in many situations. We therefore propose a novel multi-component simulation framework that treats many of the key physical mechanisms governing the dynamics of wet hair. The foundations of our approach are a discrete rod model for hair and a particle-in-cell model for fluids. To treat the thin layer of liquid that clings to the hair, we augment each hair strand with a height field representation. Our contribution is to develop the necessary physical and numerical models to evolve this new system and the interactions among its components. We develop a new reduced-dimensional liquid model to solve the motion of the liquid along the length of each hair, while accounting for its moving reference frame and influence on the hair dynamics. We derive a faithful model for surface tension-induced cohesion effects between adjacent hairs, based on the geometry of the liquid bridges that connect them. We adopt an empirically-validated drag model to treat the effects of coarse-scale interactions between hair and surrounding fluid, and propose new volume-conserving dripping and absorption strategies to transfer liquid between the reduced and particle-in-cell liquid representations. The synthesis of these techniques yields an effective wet hair simulator, which we use to animate hair flipping, an animal shaking itself dry, a spinning car wash roller brush dunked in liquid, and intricate hair coalescence effects, among several additional scenarios.


We use Houdini for surface reconstruction and rendering. The pipeline for surface reconstruction is shown here, where we convert both the particles and the reduced-liquid flow from polygons into VDBs. Then the two VDBs are combined with a union operator and smoothed with a mean-value filter.

Sample HIP File


    title={A Multi-Scale Model for Simulating Liquid-Hair Interactions},
    author={Fei, Yun (Raymond) and Maia, Henrique Teles and Batty, Christopher and Zheng, Changxi and Grinspun, Eitan},
    journal={ACM Trans. Graph.},


We thank the anonymous reviewers for their feedback. This work was supported in part by the National Science Foundation under Grant Nos.: 14-09286, 13-19483, CAREER-1453101 and Graduate Student Research Fellowship No. DGE-16-44869, the Natural Sciences and Engineering Research Council of Canada under Grant No. RGPIN-04360-2014, the National GEM Consortium, Pixar, and Adobe. We would also like to thank Peter Yichen Chen for his assistance, Ryan Goldade for advice on surface reconstruction, Xinxin Zhang for his AMGPCG pressure solver, Fang Da and Bo Zhu for insightful discussions, Daisy Nyugen, Derrick Lim for their help on storage and computational devices, Cristin Barghiel, Monika Janek, and Silvina Rocca for their technical support on SideFX Houdini.