Hair Modeling, Animation and Rendering

Hair Modeling

We present an effective method for modeling realistic curly hairstyles, taking into account both artificial hairstyling processes and natural curliness. The result is a detailed geometric model of hairs that can be rendered and animated. Our technique exploits the analogy between hairs and a vector field; interactively models global and local hair flows by superposing vector fields that have local influence. An initial model of hair strands is extracted from the superposed vector fields by tracing their field lines. Random natural or artificial curliness can be added to the initial model through a parametric hair offset function with a randomized distribution of parameters over the scalp. Techniques for shearing and clustering are also designed to improve the overall appearance of the hair model.

Hair Animation

Hair exhibits strong anisotropic dynamic properties which demand distinct dynamic models for single strands and hair-hair interactions. While a single strand can be modeled as a multibody open chain expressed in generalized coordinates, modeling hair-hair interactions is a more difficult problem. A dynamic model for this purpose is proposed based on a sparse set of guide strands. Long range connections among the strands are modeled as breakable static links formulated as nonreversible positional springs. Dynamic hair-to-hair collision is solved with the help of auxiliary triangle strips among nearby strands. Adaptive guide strands can be generated and removed on the fly to dynamically control the accuracy of a simulation. A high-quality dense hair model can be obtained at the end by transforming and interpolating the sparse guide strands.

Hair Rendering

Fine imagery of our dense hair models is rendered by considering both primary scattering and self-shadowing inside the hair volume which is modeled as being partially translucent.

Acknowledgment: the material on this webpage is based upon work partially supported by the National Science Foundation under Grant No. 0132970.