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Physics-based modelling, simulation, placement and learning for musculo-skeletal animations.

Turchet, F., 2018. Physics-based modelling, simulation, placement and learning for musculo-skeletal animations. Doctoral Thesis (Doctoral). Bournemouth University.

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TURCHET, Fabio_D.Eng._2018.pdf



In character production for Visual Effects, the realism of deformations and flesh dynamics is a vital ingredient of the final rendered moving images shown on screen. This work is a collection of projects completed at the hosting company MPC London focused on the main components needed for the animation of musculo-skeletal systems: primitives modeling, physically accurate simulation, interactive placement. Complementary projects are also presented, including the procedural modeling of wrinkles and a machine learning approach for deformable objects based on Deep Neural Networks. Primitives modeling aims at proposing an approach to generating muscle geometry complete with tendons and fibers from superficial patches sketched on the character skin mesh. The method utilizes the physics of inflatable surfaces and produces meshes ready to be tetrahedralized, that is without compenetrations. A framework for the simulation of muscles, fascia and fat tissues based on the Finite Elements Method (FEM) is presented, together with the theoretical foundations of fiber-based materials with activations and their fitting in the Implicit Euler integration. The FEM solver is then simplified in or- der to achieve interactive rates to show the potential of interactive muscle placement on the skeleton to facilitate the creation of intersection-free primitives using collision detection and resolution. Alongside physics simulation for biological tissues, the thesis explores an approach that extends the Implicit Skinning technique with wrinkles based on convolution surfaces by exploiting the gradients of the combination of bones fields. Finally, this work discusses a possible approach to the learning of physics-based deformable objects based on deep neural networks which makes use of geodesic disks convolutional layers.

Item Type:Thesis (Doctoral)
Additional Information:if you feel that this work infringes your copyright please contact the BURO Manager.
Uncontrolled Keywords:muscle simulation; deformable objects; physics-based animation; FEM; visual effects
Group:Faculty of Media & Communication
ID Code:31092
Deposited By: Symplectic RT2
Deposited On:01 Aug 2018 09:52
Last Modified:09 Aug 2022 16:04


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