<p>We present core components of a hair modeling and dynamics solution for the feature film industry. Recent research results in hair simulation are exploited as a dynamics model based on solving the Euler-Lagrange equations of motion for a discretized Cosserat curve is implemented in its entirety. Solutions to the dynamics equations are derived and a framework for symbolic integration is outlined. The resulting system is not unconditionally positive definite but requires balanced physical parameters in order to be solvable using a regular linear solver. Several implementation examples are presented, as well as a novel modeling technique based on non-linear optimization.</p>

We present core components of a hair modeling and dynamics solution for the feature film industry. Recent research results in hair simulation are exploited as a dynamics model based on solving the Euler-Lagrange equations of motion for a discretized Cosserat curve is implemented in its entirety. Solutions to the dynamics equations are derived and a framework for symbolic integration is outlined. The resulting system is not unconditionally positive definite but requires balanced physical parameters in order to be solvable using a regular linear solver. Several implementation examples are presented, as well as a novel modeling technique based on non-linear optimization.

This thesis describes a procedural hair generator that is able to generate hair from just a few hairs, called hair guides, which are directly modeled by a 3d artist. The procedural hair generator is a part of Stubble project -- a tool for hair modeling in Autodesk Maya. The procedural hair generator can generate hair during rendering, thus avoiding storage of hair geometry in a scene file, which makes the rendering process very efficient. Furthermore, hair can be generated interactively and displayed by OpenGL during modeling in Maya. Generated hair geometry is mainly defined by interpolation from the mentioned hair guides; however it is also influenced by many hair properties. These properties can change hair geometry using noise functions, define hair color, width and more. To determine hair root positions on a given triangular mesh I use my own mesh sampling algorithm that generates random samples on a triangular mesh according to a density defined by a 2-dimensional texture. My sampling algorithm uses an innovative way of sampling from a discrete probability distribution, which can be used in other applications than mesh sampling.

Creating realistic virtual hair consists of several major areas: creating the geometry, moving the hair strands realistically and rendering the hair. In this thesis, a background survey covering each one of these areas is given. A node-based, procedural hair system is presented, which utilizes the capabilities of modern GPUs. The hair system is implemented as a plugin for Autodesk Maya, and a user interface is developed to allow the user to control the various parameters. A number of nodes are developed to create effects such as clumping, noise and frizz. The proposed system can easily handle a variety of hairstyles, and pre-renders the result in real-time using a local shading model.

Hair, as a vital component of the human's appearance, plays an important role in producing digital characters. However, the generation of realistic hairstyles usually needs professional digital artists and/or complex hardware, and the procedure is often time-consuming due to its numerous numbers, and diverse hairstyles. Thus, automatic capture of real-world hairstyles with easy input can greatly benefit the production pipeline. State-of-the-art 3D hair modeling systems require either multi-view images or a single- view image with complementary synthetic 3D hair models. For the multi-view image based 3D hair reconstruction, the capture systems are often made of a large number of cameras, projectors, light sources, and are usually in the indoor environment, which prevents popular use of the methods. On the contrary, single-view image based methods only use simple capture devices, e.g.; a handheld camera. However, a front view containing a face is often required and the resulting 3D hair strand reconstruction quality is compromised. Meanwhile, several hairstyles can not be easily modeled, such as braids and kinky hairstyles (afro-textured hairs), even though they are very common in real life. In this dissertation, we implement a single-view imaged based 3D hair modeling system, where our hair reconstruction is done through 2D hair analysis and 3D strands creation, which benefits from both traditional image processing techniques and the strengths of machine learning. Our 2D hair analysis is used to learn the attributes of input hairs, including 2D hair strands, detailed hairstyle patterns, and the corresponding parametric representation (which includes braids and kinky hairs), and braid structures. Simultaneously 3D hair strands are generated using deep-learning models. Our method is different from previous methods as our generated hair models can be modified by controlling the attributes and parameters we learned from the 2D hair recognition/analysis system. Our system does not require a face to be shown in the input image and to our best knowledge, our work is the first work that can reconstruct 3D braided hair and kinky hair given a single-view image. Qualitatively and quantitatively assessments indicate that our system can generate a variety of realistic 3D hairstyle models.

Despite the importance of hair for characters in games, the process for creating realistic looking real-time hair is a highly difficult task that can only be performed by a few artists. As the industry is growing so does expectations of games graphics and the need for higher fidelity hairstyles. This paper sets out to explain current modeling techniques for hairstyling, and presents an alternate way for styling haircard models using curve deformers. Our Blende radd-on, based on this method is then previewed by industry professionals to evaluate their perception of the method’s effectiveness based on “control”, “time” and “preference”. Our conclusion is that per wisp curve based controls are easily learnt, relatively quick to use and gives adequate control over each haircard. The method therefore shows promise instream lining artists’ production pipelines. This test serves as a pilot for further exploration of curve based tools for production of real-time hair.