An Image-Based Hair Modeling and Dynamic Simulation Method
PubDate: June 2017
Teams: Beihang University
Writers: Yongtang Bao; Yue Qi
In the past years, significant progress has been made in image-based hair modeling, thus producing abundant 3-D hair models. However, on the one hand, the reconstructed hair models could not preserve the structural details of hairstyle. On the other hand, there exists little research on these modeling results. Currently, hair geometry is mostly represented as mass chains of 3-D points. It is difficult to simulate hair directly from this representation. In this paper, we propose a novel approach to convert hair geometry model into helices, which could be easily plugged into dynamic hair simulation. We construct a hair model from a hybrid orientation field, which is generated from four fields. We extract a representative guide hair strand model from this hair geometry. Then, we use adaptive floating tangents fitting algorithm to convert this hair geometry into a physics-based hair model. To initialize this hair model, we calculate a corresponding static equilibrium configuration under external forces, including gravity, frictional contacts, and viscous drag from ambient air. We simulate dynamic hair by the Euler-Lagrange equations. Our approach can preserve structural details of 3-D hair models, and can be applied to simulate various hair geometries.