Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.753164
Title: Physics-based modelling, simulation, placement and learning for musculo-skeletal animations
Author: Turchet, Fabio
ISNI:       0000 0004 7426 2708
Awarding Body: Bournemouth University
Current Institution: Bournemouth University
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.753164  DOI: Not available
Share: