Citation
Md Ali, Nor Azura
(2015)
Improving embedded linear elasticity in deformable models using stiffness matrix.
Masters thesis, Universiti Putra Malaysia.
Abstract
Physical animation deformation is an important part of computer animation. Most geometric models commonly used in graphics have hundreds of thousands of vertices. Embedding is also a good approach because of its simplicity and ability to preserve geometric features but a standard embedding technique does not correctly model geometry with complex branching. Complex models may have a lot of parts with different properties of different materials. In such cases, it is more likely that a coarse element will contain a mix of materials, soft and hard, and not just one material. Therefore, it is difficult to select an appropriate material in the element, whether stiff or soft, that will deform in the same manner. Thus, many GPU-based collision detection algorithms have been limited to examining the circumstances of the collision in discrete time. In this research, embedding of a linear elastic deformable model is presented. This research has resulted in a significant improvement in efficient animation based on physical objects that are very detailed. To perform embedding, topology information should be taken into account. This means that parts of disconnected elements that fall into the same coarse element can be animated freely. Thus, the properties of different materials are accounted for by calculating the interpolation function together with appropriate stiffness for the coarse elements that are similar to the embedded material. Finally, coarse embedding space is also included to provide a better animation of the border. The result is a simple approach to a complex deformation simulation model with ease and speed associated with coarse regular embedding, with quality and detail that can be made at a finer resolution. Finally, in order to obtain better GPU processing time compared to the computer, an anisotropic visco-hyperelastic constitutive formulation is presented for implementation in a graphical processor unit (GPU).
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