The application of three-dimensional mass-spring structures in the real-time simulation of sheet materials for computer generated imagery

Despite the resources devoted to computer graphics technology over the last 40 years, there is still a need to increase the realism with which flexible materials are simulated. However, to date reported methods are restricted in their application by their use of two-dimensional structures and implicit integration methods that lend themselves to modelling cloth-like sheets but not stiffer, thicker materials in which bending moments play a significant role. This thesis presents a real-time, computationally efficient environment for simulations of sheet materials. The approach described differs from other techniques principally through its novel use of multilayer sheet structures. In addition to more accurately modelling bending moment effects, it also allows the effects of increased temperature within the environment to be simulated. Limitations of this approach include the increased difficulties of calibrating a realistic and stable simulation compared to implicit based methods. A series of experiments are conducted to establish the effectiveness of the technique, evaluating the suitability of different integration methods, sheet structures, and simulation parameters, before conducting a Human Computer Interaction (HCI) based evaluation to establish the effectiveness with which the technique can produce credible simulations. These results are also compared against a system that utilises an established method for sheet simulation and a hybrid solution that combines the use of 3D (i.e. multilayer) lattice structures with the recognised sheet simulation approach. The results suggest that the use of a three-dimensional structure does provide a level of enhanced realism when simulating stiff laminar materials although the best overall results were achieved through the use of the hybrid model

Multi-Scale Models to Simulate Interactions between Liquid and Thin Structures

In this dissertation, we introduce a framework for simulating the dynamics between liquid and thin structures, including the effects of buoyancy, drag, capillary cohesion, dripping, and diffusion. After introducing related works, Part I begins with a discussion on the interactions between Newtonian fluid and fabrics. In this discussion, we treat both the fluid and the fabrics as continuum media; thus, the physical model is built from mixture theory. In Part II, we discuss the interactions between Newtonian fluid and hairs. To have more detailed dynamics, we no longer treat the hairs as continuum media. Instead, we treat them as discrete Kirchhoff rods. To deal with the thin layer of liquid that clings to the hairs, we augment each hair strand with a height field representation, through which we introduce a new reduced-dimensional flow model to solve the motion of liquid along the longitudinal direction of each hair. In addition, we develop a faithful model for the hairs' cohesion induced by surface tension, where a penalty force is applied to simulate the collision and cohesion between hairs. To enable the discrete strands interact with continuum-based, shear-dependent liquid, in Part III, we develop models that account for the volume change of the liquid as it passes through strands and the momentum exchange between the strands and the liquid. Accordingly, we extend the reduced-dimensional flow model to simulate liquid with elastoviscoplastic behavior. Furthermore, we use a constraint-based model to replace the penalty-force model to handle contact, which enables an accurate simulation of the frictional and adhesive effects between wet strands. We also present a principled method to preserve the total momentum of a strand and its surface flow, as well as an analytic plastic flow approach for Herschel-Bulkley fluid that enables stable semi-implicit integration at larger time steps. We demonstrate a wide range of effects, including the challenging animation scenarios involving splashing, wringing, and colliding of wet clothes, as well as flipping of hair, animals shaking, spinning roller brushes from car washes being dunked in water, and intricate hair coalescence effects. For complex liquids, we explore a series of challenging scenarios, including strands interacting with oil paint, mud, cream, melted chocolate, and pasta sauce

Towards Real-Time Simulation Of Hyperelastic Materials

We propose a new method for physics-based simulation supporting many different types of hyperelastic materials from mass-spring systems to three-dimensional finite element models, pushing the performance of the simulation towards real-time. Fast simulation methods such as Position Based Dynamics exist, but support only limited selection of materials; even classical materials such as corotated linear elasticity and Neo-Hookean elasticity are not supported. Simulation of these types of materials currently relies on Newton\u27s method, which is slow, even with only one iteration per timestep. In this work, we start from simple material models such as mass-spring systems or as-rigid-as-possible materials. We express the widely used implicit Euler time integration as an energy minimization problem and introduce auxiliary projection variables as extra unknowns. After our reformulation, the minimization problem becomes linear in the node positions, while all the non-linear terms are isolated in individual elements. We then extend this idea to efficiently simulate a more general spatial discretization using finite element method. We show that our reformulation can be interpreted as a quasi-Newton method. This insight enables very efficient simulation of a large class of hyperelastic materials. The quasi-Newton interpretation also allows us to leverage ideas from numerical optimization. In particular, we show that our solver can be further accelerated using L-BFGS updates (Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm). Our final method is typically more than ten times faster than one iteration of Newton\u27s method without compromising quality. In fact, our result is often more accurate than the result obtained with one iteration of Newton\u27s method. Our method is also easier to implement, implying reduced software development costs

Three dimensional simulation of cloth drape

Research has been carried out in the study of cloth modelling over many decades. The more recent arrival of computers however has meant that the necessary complex calculations can be performed quicker and that visual display of the results is more realistic than for the earlier models. Today's textile and garment designers are happy to use the latest two dimensional design and display technology to create designs and experiment with patterns and colours. The computer is seen as an additional tool that performs some of the more tedious jobs such as re-drawing, re-colouring and pattern sizing. Designers have the ability and experience to visualise their ideas without the need for photo reality. However the real garment must be created when promoting these ideas to potential customers. Three dimensional computer visualisation of a garment can remove the need to create the garment until after the customer has placed an order. As well as reducing costs in the fashion industry, realistic three dimensional cloth animation has benefits for the computer games and film industries. This thesis describes the development of a realistic cloth drape model. The system uses the Finite Element Method for the draping equations and graphics routines to enhance the visual display. During the research the problem of collision detection and response involving dynamic models has been tackled and a unique collision detection method has been developed. This method has proved very accurate in the simulation of cloth drape over a body model and is also described in the thesis. Three dimensional design and display are seen as the next logical steps to current two dimensional practices in the textiles industry. This thesis outlines current and previous cloth modelling studies carried out by other research groups. It goes on to provide a full description of the drape method that has been developed during this research period

Puppets between human, animal and machine: towards the modes of movement contesting the anthropocentric view of life in animation

In this PhD thesis, I challenge animation studies’ conventional notion that animation can bring something inanimate to “life”. This emphasis on animation’s capacity to make a figure appear to move on screen has led to the problematic notion that movement has a synonymous relationship with life. Contesting these discourses, I show in this thesis that not every animated figure suggests the impression of life. In order to prove this, I put forward as a critical focus the puppet-as-puppet figure, that is, the figure of a puppet depicted as a puppet per se in the film diegesis, which problematises the impression of life even if appearing to move on screen. A related focus in my thesis is the mode of movement which functions as a visual and physical parameter in order to analyse what an animated (or static) figure is intended to look like, instead of reducing it to a question of life. Through case studies of these puppet-as-puppet figures, which I classify into four groups, I examine the varying ways in which they are depicted as inanimate or sub/nonhuman, even when in human form, in contrast to human or (anthropomorphic) animal figures, both in terms of their mode of movement as well as their appearance. Examining how these depictions demonstrate anthropocentric views of puppets, I consider religio-philosophical, scientific and aesthetic discourses on puppets and human/animal simulacra. Further, I explore a selection of puppet-as-puppet figures as alternatives to these anthropocentric conventions, examining their defamiliarisation of the animating human subject’s mastery over the animated non/subhuman object, and the non-anthropocentric sensations which their movements arouse on screen in the relationship between humanity and materiality

Quasi second-order methods for PDE-constrained forward and inverse problems

La conception assistée par ordinateur (CAO), les effets visuels, la robotique et de nombreux autres domaines tels que la biologie computationnelle, le génie aérospatial, etc. reposent sur la résolution de problèmes mathématiques. Dans la plupart des cas, des méthodes de calcul sont utilisées pour résoudre ces problèmes. Le choix et la construction de la méthode de calcul ont un impact important sur les résultats et l'efficacité du calcul. La structure du problème peut être utilisée pour créer des méthodes, qui sont plus rapides et produisent des résultats qualitativement meilleurs que les méthodes qui n'utilisent pas la structure. Cette thèse présente trois articles avec trois nouvelles méthodes de calcul s'attaquant à des problèmes de simulation et d'optimisation contraints par des équations aux dérivées partielles (EDP). Dans le premier article, nous abordons le problème de la dissipation d'énergie des solveurs fluides courants dans les effets visuels. Les solveurs de fluides sont omniprésents dans la création d'effets dans les courts et longs métrages d'animation. Nous présentons un schéma d'intégration temporelle pour la dynamique des fluides incompressibles qui préserve mieux l'énergie comparé aux nombreuses méthodes précédentes. La méthode présentée présente une faible surcharge et peut être intégrée à un large éventail de méthodes existantes. L'amélioration de la conservation de l'énergie permet la création d'animations nettement plus dynamiques. Nous abordons ensuite la conception computationelle dont le but est d'exploiter l'outils computationnel dans le but d'améliorer le processus de conception. Plus précisément, nous examinons l'analyse de sensibilité, qui calcule les sensibilités du résultat de la simulation par rapport aux paramètres de conception afin d'optimiser automatiquement la conception. Dans ce contexte, nous présentons une méthode efficace de calcul de la direction de recherche de Gauss-Newton, en tirant parti des solveurs linéaires directs épars modernes. Notre méthode réduit considérablement le coût de calcul du processus d'optimisation pour une certaine classe de problèmes de conception inverse. Enfin, nous examinons l'optimisation de la topologie à l'aide de techniques d'apprentissage automatique. Nous posons deux questions : Pouvons-nous faire de l'optimisation topologique sans maillage et pouvons-nous apprendre un espace de solutions d'optimisation topologique. Nous appliquons des représentations neuronales implicites et obtenons des résultats structurellement sensibles pour l'optimisation topologique sans maillage en guidant le réseau neuronal pendant le processus d'optimisation et en adaptant les méthodes d'optimisation topologique par éléments finis. Notre méthode produit une représentation continue du champ de densité. De plus, nous présentons des espaces de solution appris en utilisant la représentation neuronale implicite.Computer-aided design (CAD), visual effects, robotics and many other fields such as computational biology, aerospace engineering etc. rely on the solution of mathematical problems. In most cases, computational methods are used to solve these problems. The choice and construction of the computational method has large impact on the results and the computational efficiency. The structure of the problem can be used to create methods, that are faster and produce qualitatively better results than methods that do not use the structure. This thesis presents three articles with three new computational methods tackling partial differential equation (PDE) constrained simulation and optimization problems. In the first article, we tackle the problem of energy dissipation of common fluid solvers in visual effects. Fluid solvers are ubiquitously used to create effects in animated shorts and feature films. We present a time integration scheme for incompressible fluid dynamics which preserves energy better than many previous methods. The presented method has low overhead and can be integrated into a wide range of existing methods. The improved energy conservation leads to noticeably more dynamic animations. We then move on to computational design whose goal is to harnesses computational techniques for the design process. Specifically, we look at sensitivity analysis, which computes the sensitivities of the simulation result with respect to the design parameters to automatically optimize the design. In this context, we present an efficient way to compute the Gauss-Newton search direction, leveraging modern sparse direct linear solvers. Our method reduces the computational cost of the optimization process greatly for a certain class of inverse design problems. Finally, we look at topology optimization using machine learning techniques. We ask two questions: Can we do mesh-free topology optimization and can we learn a space of topology optimization solutions. We apply implicit neural representations and obtain structurally sensible results for mesh-free topology optimization by guiding the neural network during optimization process and adapting methods from finite element based topology optimization. Our method produces a continuous representation of the density field. Additionally, we present learned solution spaces using the implicit neural representation