In character animation achieving realistic deformations of the skin is a challenging task. Geometric skinning techniques, such as smooth blending or dual-quaternions, are very popular for their high performance but fail to produce convincing deformations. They look too soft compared to human skin deformation at a rigid bone joint. In addition advanced effects such as skin contacts or bulges are not taken into account. Other methods make use of physical simulation or volume control to better capture the skin behavior, yet they cannot deliver real-time feedback. We developed a novel skinning framework called implicit skinning. Our method produces visually plausible deformations in real-time by handling realistic skin contacts and bulges between limbs. Implicit skinning exploits the ability of implicit surfaces to be robustly combined as well as their efficient collision detection. By approximating the mesh by a set of implicit surfaces, we are able to guide the deformation of a mesh character. we can combine the implicit surfaces in real-time, and use the final implicit surface to adjust the position of mesh vertices at each animation step. Since collision detection is very efficient using implicit surfaces we achieve skin contacts between limbs at interactive to real-time frame rates. In this thesis we present the complete implicit skinning framework, that is, the conversion of a mesh character to implicit surfaces, the composition operators and the mesh deformation algorithm on top of the implicit surface. Two deformation algorithms are studied: a fast history dependent algorithm which acts as a post process on top of dual-quaternions skinning and a slower yet more robust history dependent algorithm. / Graduate

This thesis work will investigate and implement a method for reconstructing an unstructured mesh sequence with evolving topology. The goal of the method is to increase frame-to-frame coherency of the triangulation. The motivation of the method is that many of current state-of-the-art mesh compression and decimation algorithms for mesh sequences are based on static connectivity.

Ester liquids including natural ester and synthetic ester are considered as potential substitutes for mineral oil, due to their good biodegradability and high fire points. Although these liquids have been widely used in distribution and traction transformers, research efforts are required for the purposes of design and manufacture of high voltage and large power transformers which are filled by esters. Indeed, it would be risky to apply esters in large power transformers without thorough understandings of their behaviours in large gaps and/or when combined with pressboard insulation. Therefore, investigations of creepage discharges along the surface of pressboard in esters are vitally important and their behaviours should be compared with those of mineral oils. This thesis is aimed to investigate the creepage discharges along pressboard in esters and mineral oil under ac divergent electric field. Apparent charges, current signals and images of streamer channels were obtained synchronously to identify whether and how the introduction of pressboard surface would influence the inception and propagation of discharges as compared to tests in open gap. When over-stressed by higher voltages, the surface tracking along the pressboard-ester interface, triggered by sustaining creepage discharges, was studied and the evolutions of accompanying creepage discharge patterns were investigated. In these experiments, both esters and mineral oil impregnated pressboards were comparatively studied. The test results indicated that at the inception stage, the presence of pressboard or any other solid types in different liquids under test do not influence the PD inception voltages; in the propagation stage, solid surface tends to promote the development of discharges, especially those occurring in negative half cycles, and shifts more discharges towards the zero-crossing phase angles. This discharge promotion effect is much more evident in esters than in mineral oil, probably because of higher discharge intensity in esters and higher viscosity of esters. The space charge effect and the residual low density channel effect are proved as the mechanisms best explaining the influences of solids on creepage discharges. Under higher voltages, it was found that the impregnated pressboard is susceptible to discharge erosion characterized by “white and carbonized tree-shaped marks”, due to intense discharges occurring on or near the pressboard surface. The “white mark” appears at a lower voltage and propagates more easily on ester impregnated pressboard. The gaseous “white mark” channels will attract the subsequent discharges to follow the same discharge routes; the accumulative energy dissipation in these channels will then result in the carbonization of the channels. Once formed, the surface tree-shaped mark can continue to grow even under reduced voltage levels until it bridges the gap and causes the final flashover.

Dans cette thèse nous proposons des algorithmes pour le suivi 3D du mouvement des objects déformables à partir de plusieurs caméras vidéo. Bien qu’une suite de reconstructions tridimensionnelles peut être obtenue par des méthodes de reconstruction statique, celle-ci ne représente pas le mouvement. Nous voulons produire une représentation temporellement cohérente de la suite de formes prises par l’object. Précisément, nous souhaitons représenter l’objet par une surface maillée 3D dont les sommets se déplacent au cours du temps mais dont la topologie reste identique.Contrairement à beaucoup d’approches existantes, nous proposons de représenter le mouvement du volume intérieur des formes, dans le but de mieux représenter la nature volumétrique des objets. Nous traitons de manière volumétrique les problèmes fondamentaux du suivi déformable que sont l’association d’éléments semblables entre deux formes et la modélisation de la déformation. En particulier, nous adaptons au formes volumétriques les modèles d’association EM-ICP non-rigide ansi que l’association par détection par apprentissage automatique.D’autre part, nous abordons la question de la modélisation de l’évolution temporelle de la déformation au cours d’une séquence dans le but de mieux contraindre le problème du suivi temporel. Pour cela, nous modélisons un espace de forme construit autour de propriétés de déformations locales que nous apprenons automatiqument lors du suivi.Nous validons nos algorithmes de suivi sur des séquences vidéo multi-caméras avec vérité terrain (silhouettes et suivi par marqueurs). Nos résultats se révèlent meilleurs ou équivalents à ceux obtenus avec les méthodes de l’état de l’art.Enfin, nous démontrons que le suivi volumétrique et la représentation que nous avons choisie permettent de produire des animations 3D qui combinent l’acquisition et la simulation de mouvement. / In this thesis we propose algorithms for tracking 3D deformable shapes in motion from multiview video. Although series of reconstructed 3D shapes can be obtained by applying a static reconstruction algorithm to each temporal frame independently, such series do not represent motion. Instead, we want to provide a temporally coherent representation of the sequence of shapes resulting from temporal evolutions of a shape. Precisely, we want to represent the observed shape sequence as a 3D surface mesh whose vertices move in time but whose topology is constant.In contrast with most existing approaches, we propose to represent the motion of inner shape volumes, with the aim of better accounting for the volumetric nature of the observed object. We provide a fully volumetric approach to the fundamental problems of deformable shape tracking, which are the association between corresponding shape elements and the deformation model. In particular, we extend to a volumetric shape representation the EM-ICP tracking framework and the association-by-detection strategy.Furthermore, in order to better constrain the shape tracking problem, we propose a model for the temporal evolution of deformation. Our deformation model defines a shape space parametrized by variables that capture local deformation properties of the shape and whose values are automatically learned during the tracking process.We validate our tracking algorithms on several multiview video sequences with ground truth (silhouette and marker-based tracking). Our results are better or comparable to state of the art approaches.Finally, we show that volumetric tracking and the shape representation we choose can be leveraged for producing shape animations which combine captured and simulatated motion.

In this thesis we address the problem of digitizing the motion of three-dimensional shapes that move and deform in time. These shapes are observed from several points of view with cameras that record the scene's evolution as videos. Using available reconstruction methods, these videos can be converted into a sequence of three-dimensional snapshots that capture the appearance and shape of the objects in the scene. The focus of this thesis is to complement appearance and shape with information on the motion and deformation of objects. In other words, we want to measure the trajectory of every point on the observed surfaces. This is a challenging problem because the captured videos are only sequences of images, and the reconstructed shapes are built independently from each other. While the human brain excels at recreating the illusion of motion from these snapshots, using them to automatically measure motion is still largely an open problem. The majority of prior works on the subject has focused on tracking the performance of one human actor, and used the strong prior knowledge on the articulated nature of human motion to handle the ambiguity and noise inherent to visual data. In contrast, the presented developments consist of generic methods that allow to digitize scenes involving several humans and deformable objects of arbitrary nature. To perform surface tracking as generically as possible, we formulate the problem as the geometric registration of surfaces and deform a reference mesh to fit a sequence of independently reconstructed meshes. We introduce a set of algorithms and numerical tools that integrate into a pipeline whose output is an animated mesh. Our first contribution consists of a generic mesh deformation model and numerical optimization framework that divides the tracked surface into a collection of patches, organizes these patches in a deformation graph and emulates elastic behavior with respect to the reference pose. As a second contribution, we present a probabilistic formulation of deformable surface registration that embeds the inference in an Expectation-Maximization framework that explicitly accounts for the noise and in the acquisition. As a third contribution, we look at how prior knowledge can be used when tracking articulated objects, and compare different deformation model with skeletal-based tracking. The studies reported by this thesis are supported by extensive experiments on various 4D datasets. They show that in spite of weaker assumption on the nature of the tracked objects, the presented ideas allow to process complex scenes involving several arbitrary objects, while robustly handling missing data and relatively large reconstruction artifacts.

Cette thèse traite du suivi temporel de surfaces déformables. Ces surfaces sont observées depuis plusieurs points de vue par des caméras qui capturent l'évolution de la scène et l'enregistrent sous la forme de vidéos. Du fait des progrès récents en reconstruction multi-vue, cet ensemble de vidéos peut être converti en une série de clichés tridimensionnels qui capturent l'apparence et la forme des objets dans la scène. Le problème au coeur des travaux rapportés par cette thèse est de complémenter les informations d'apparence et de forme avec des informations sur les mouvements et les déformations des objets. En d'autres mots, il s'agit de mesurer la trajectoire de chacun des points sur les surfaces observées. Ceci est un problème difficile car les vidéos capturées ne sont que des séquences d'images, et car les formes reconstruites à chaque instant le sont indépendemment les unes des autres. Si le cerveau humain excelle à recréer l'illusion de mouvement à partir de ces clichés, leur utilisation pour la mesure automatisée du mouvement reste une question largement ouverte. La majorité des précédents travaux sur le sujet se sont focalisés sur la capture du mouvement humain et ont bénéficié de la nature articulée de ce mouvement qui pouvait être utilisé comme a-priori dans les calculs. La spécificité des développements présentés ici réside dans la généricité des méthodes qui permettent de capturer des scènes dynamiques plus complexes contenant plusieurs acteurs et différents objets déformables de nature inconnue a priori. Pour suivre les surfaces de la façon la plus générique possible, nous formulons le problème comme celui de l'alignement géométrique de surfaces, et déformons un maillage de référence pour l'aligner avec les maillages indépendemment reconstruits de la séquence. Nous présentons un ensemble d'algorithmes et d'outils numériques intégrés dans une chaîne de traitements dont le résultat est un maillage animé. Notre première contribution est une méthode de déformation de maillage qui divise la surface en une collection de morceaux élémentaires de surfaces que nous nommons patches. Ces patches sont organisés dans un graphe de déformation, et une force est appliquée sur cette structure pour émuler une déformation élastique par rapport à la pose de référence. Comme seconde contribution, nous présentons une formulation probabiliste de l'alignement de surfaces déformables qui modélise explicitement le bruit dans le processus d'acquisition. Pour finir, nous étudions dans quelle mesure les a-prioris sur la nature articulée du mouvement peuvent aider, et comparons différents modèles de déformation à une méthode de suivi de squelette. Les développements rapportés par cette thèse sont validés par de nombreuses expériences sur une variété de séquences. Ces résultats montrent qu'en dépit d'a-prioris moins forts sur les surfaces suivies, les idées présentées permettent de traiter des scènes complexes contenant de multiples objets tout en se comportant de façon robuste vis-a-vis de données fragmentaires et d'erreurs de reconstruction. / In this thesis we address the problem of digitizing the motion of three-dimensional shapes that move and deform in time. These shapes are observed from several points of view with cameras that record the scene's evolution as videos. Using available reconstruction methods, these videos can be converted into a sequence of three-dimensional snapshots that capture the appearance and shape of the objects in the scene. The focus of this thesis is to complement appearance and shape with information on the motion and deformation of objects. In other words, we want to measure the trajectory of every point on the observed surfaces. This is a challenging problem because the captured videos are only sequences of images, and the reconstructed shapes are built independently from each other. While the human brain excels at recreating the illusion of motion from these snapshots, using them to automatically measure motion is still largely an open problem. The majority of prior works on the subject has focused on tracking the performance of one human actor, and used the strong prior knowledge on the articulated nature of human motion to handle the ambiguity and noise inherent to visual data. In contrast, the presented developments consist of generic methods that allow to digitize scenes involving several humans and deformable objects of arbitrary nature. To perform surface tracking as generically as possible, we formulate the problem as the geometric registration of surfaces and deform a reference mesh to fit a sequence of independently reconstructed meshes. We introduce a set of algorithms and numerical tools that integrate into a pipeline whose output is an animated mesh. Our first contribution consists of a generic mesh deformation model and numerical optimization framework that divides the tracked surface into a collection of patches, organizes these patches in a deformation graph and emulates elastic behavior with respect to the reference pose. As a second contribution, we present a probabilistic formulation of deformable surface registration that embeds the inference in an Expectation-Maximization framework that explicitly accounts for the noise and in the acquisition. As a third contribution, we look at how prior knowledge can be used when tracking articulated objects, and compare different deformation model with skeletal-based tracking. The studies reported by this thesis are supported by extensive experiments on various 4D datasets. They show that in spite of weaker assumption on the nature of the tracked objects, the presented ideas allow to process complex scenes involving several arbitrary objects, while robustly handling missing data and relatively large reconstruction artifacts.