Sparse Volumetric Deformation

Volume rendering is becoming increasingly popular as applications require realistic solid shape representations with seamless texture mapping and accurate filtering. However rendering sparse volumetric data is difficult because of the limited memory and processing capabilities of current hardware. To address these limitations, the volumetric information can be stored at progressive resolutions in the hierarchical branches of a tree structure, and sampled according to the region of interest. This means that only a partial region of the full dataset is processed, and therefore massive volumetric scenes can be rendered efficiently. The problem with this approach is that it currently only supports static scenes. This is because it is difficult to accurately deform massive amounts of volume elements and reconstruct the scene hierarchy in real-time. Another problem is that deformation operations distort the shape where more than one volume element tries to occupy the same location, and similarly gaps occur where deformation stretches the elements further than one discrete location. It is also challenging to efficiently support sophisticated deformations at hierarchical resolutions, such as character skinning or physically based animation. These types of deformation are expensive and require a control structure (for example a cage or skeleton) that maps to a set of features to accelerate the deformation process. The problems with this technique are that the varying volume hierarchy reflects different feature sizes, and manipulating the features at the original resolution is too expensive; therefore the control structure must also hierarchically capture features according to the varying volumetric resolution. This thesis investigates the area of deforming and rendering massive amounts of dynamic volumetric content. The proposed approach efficiently deforms hierarchical volume elements without introducing artifacts and supports both ray casting and rasterization renderers. This enables light transport to be modeled both accurately and efficiently with applications in the fields of real-time rendering and computer animation. Sophisticated volumetric deformation, including character animation, is also supported in real-time. This is achieved by automatically generating a control skeleton which is mapped to the varying feature resolution of the volume hierarchy. The output deformations are demonstrated in massive dynamic volumetric scenes

Relationship descriptors for interactive motion adaptation

In this thesis we present an interactive motion adaptation scheme for close interactions between skeletal characters and mesh structures, such as navigating restricted environments and manipulating tools. We propose a new spatial-relationship based representation to encode character-object interactions describing the kinematics of the body parts by the weighted sum of vectors relative to descriptor points selectively sampled over the scene. In contrast to previous discrete representations that either only handle static spatial relationships, or require offline, costly optimization processes, our continuous framework smoothly adapts the motion of a character to deformations in the objects and character morphologies in real-time whilst preserving the original context and style of the scene. We demonstrate the strength of working in our relationship-descriptor space in tackling the issue of motion editing under large environment deformations by integrating procedural animation techniques such as repositioning contacts in an interaction whilst preserving the context and style of the original animation. Furthermore we propose a method that can be used to adapt animations from template objects to novel ones by solving for mappings between the two in our relationship-descriptor space effectively transferring an entire motion from one object to a new one of different geometry whilst ensuring continuity across all frames of the animation, as opposed to mapping static poses only as is traditionally achieved. The experimental results show that our method can be used for a wide range of applications, including motion retargeting for dynamically changing scenes, multi-character interactions, and interactive character control and deformation transfer for scenes that involve close interactions. We further demonstrate a key use case in retargeting locomotion to uneven terrains and curving paths convincingly for bipeds and quadrupeds. Our framework is useful for artists who need to design animated scenes interactively, and modern computer games that allow users to design their own virtual characters, objects and environments, such that they can recycle existing motion data for a large variety of different configurations without the need to manually reconfigure motion from scratch or store expensive combinations of animation in memory. Most importantly it’s achieved in real-time

Mathematical Imaging and Surface Processing

Within the last decade image and geometry processing have become increasingly rigorous with solid foundations in mathematics. Both areas are research fields at the intersection of different mathematical disciplines, ranging from geometry and calculus of variations to PDE analysis and numerical analysis. The workshop brought together scientists from all these areas and a fruitful interplay took place. There was a lively exchange of ideas between geometry and image processing applications areas, characterized in a number of ways in this workshop. For example, optimal transport, first applied in computer vision is now used to define a distance measure between 3d shapes, spectral analysis as a tool in image processing can be applied in surface classification and matching, and so on. We have also seen the use of Riemannian geometry as a powerful tool to improve the analysis of multivalued images. This volume collects the abstracts for all the presentations covering this wide spectrum of tools and application domains

Towards Predictive Rendering in Virtual Reality

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation

Patterns in Motion - From the Detection of Primitives to Steering Animations

In recent decades, the world of technology has developed rapidly. Illustrative of this trend is the growing number of affrdable methods for recording new and bigger data sets. The resulting masses of multivariate and high-dimensional data represent a new challenge for research and industry. This thesis is dedicated to the development of novel methods for processing multivariate time series data, thus meeting this Data Science related challenge. This is done by introducing a range of different methods designed to deal with time series data. The variety of methods re ects the different requirements and the typical stage of data processing ranging from pre-processing to post- processing and data recycling. Many of the techniques introduced work in a general setting. However, various types of motion recordings of human and animal subjects were chosen as representatives of multi-variate time series. The different data modalities include Motion Capture data, accelerations, gyroscopes, electromyography, depth data (Kinect) and animated 3D-meshes. It is the goal of this thesis to provide a deeper understanding of working with multi-variate time series by taking the example of multi-variate motion data. However, in order to maintain an overview of the matter, the thesis follows a basic general pipeline. This pipeline was developed as a guideline for time series processing and is the first contribution of this work. Each part of the thesis represents one important stage of this pipeline which can be summarized under the topics segmentation, analysis and synthesis. Specific examples of different data modalities, processing requirements and methods to meet those are discussed in the chapters of the respective parts. One important contribution of this thesis is a novel method for temporal segmentation of motion data. It is based on the idea of self-similarities within motion data and is capable of unsupervised segmentation of range of motion data into distinct activities and motion primitives. The examples concerned with the analysis of multi-variate time series re ect the role of data analysis in different inter-disciplinary contexts and also the variety of requirements that comes with collaboration with other sciences. These requirements are directly connected to current challenges in data science. Finally, the problem of synthesis of multi-variate time series is discussed using a graph-based example and examples related to rigging or steering of meshes. Synthesis is an important stage in data processing because it creates new data from existing ones in a controlled way. This makes exploiting existing data sets and and access of more condensed data possible, thus providing feasible alternatives to otherwise time-consuming manual processing.Muster in Bewegung - Von der Erkennung von Primitiven zur Steuerung von Animationen In den letzten Jahrzehnten hat sich die Welt der Technologie rapide entwickelt. Beispielhaft für diese Entwicklung ist die wachsende Zahl erschwinglicher Methoden zum Aufzeichnen neuer und immer größerer Datenmengen. Die sich daraus ergebenden Massen multivariater und hochdimensionaler Daten stellen Forschung wie Industrie vor neuartige Probleme. Diese Arbeit ist der Entwicklung neuer Verfahren zur Verarbeitung multivariater Zeitreihen gewidmet und stellt sich damit einer großen Herausforderung, welche unmittelbar mit dem neuen Feld der sogenannten Data Science verbunden ist. In ihr werden ein Reihe von verschiedenen Verfahren zur Verarbeitung multivariater Zeitserien eingeführt. Die verschiedenen Verfahren gehen jeweils auf unterschiedliche Anforderungen und typische Stadien der Datenverarbeitung ein und reichen von Vorverarbeitung bis zur Nachverarbeitung und darüber hinaus zur Wiederverwertung. Viele der vorgestellten Techniken eignen sich zur Verarbeitung allgemeiner multivariater Zeitreihen. Allerdings wurden hier eine Anzahl verschiedenartiger Aufnahmen von menschlichen und tierischen Subjekte ausgewählt, welche als Vertreter für allgemeine multivariate Zeitreihen gelten können. Zu den unterschiedlichen Modalitäten der Aufnahmen gehören Motion Capture Daten, Beschleunigungen, Gyroskopdaten, Elektromyographie, Tiefenbilder ( Kinect ) und animierte 3D -Meshes. Es ist das Ziel dieser Arbeit, am Beispiel der multivariaten Bewegungsdaten ein tieferes Verstndnis für den Umgang mit multivariaten Zeitreihen zu vermitteln. Um jedoch einen Überblick ber die Materie zu wahren, folgt sie jedoch einer grundlegenden und allgemeinen Pipeline. Diese Pipeline wurde als Leitfaden für die Verarbeitung von Zeitreihen entwickelt und ist der erste Beitrag dieser Arbeit. Jeder weitere Teil der Arbeit behandelt eine von drei größeren Stationen in der Pipeline, welche sich unter unter die Themen Segmentierung, Analyse und Synthese eingliedern lassen. Beispiele verschiedener Datenmodalitäten und Anforderungen an ihre Verarbeitung erläutern die jeweiligen Verfahren. Ein wichtiger Beitrag dieser Arbeit ist ein neuartiges Verfahren zur zeitlichen Segmentierung von Bewegungsdaten. Dieses basiert auf der Idee der Selbstähnlichkeit von Bewegungsdaten und ist in der Lage, verschiedenste Bewegungsdaten voll-automatisch in unterschiedliche Aktivitäten und Bewegungs-Primitive zu zerlegen. Die Beispiele fr die Analyse multivariater Zeitreihen spiegeln die Rolle der Datenanalyse in verschiedenen interdisziplinären Zusammenhänge besonders wider und illustrieren auch die Vielfalt der Anforderungen, die sich in interdisziplinären Kontexten auftun. Schließlich wird das Problem der Synthese multivariater Zeitreihen unter Verwendung eines graph-basierten und eines Steering Beispiels diskutiert. Synthese ist insofern ein wichtiger Schritt in der Datenverarbeitung, da sie es erlaubt, auf kontrollierte Art neue Daten aus vorhandenen zu erzeugen. Dies macht die Nutzung bestehender Datensätze und den Zugang zu dichteren Datenmodellen möglich, wodurch Alternativen zur ansonsten zeitaufwendigen manuellen Verarbeitung aufgezeigt werden

Iterative Solvers for Physics-based Simulations and Displays

La génération d’images et de simulations réalistes requiert des modèles complexes pour capturer tous les détails d’un phénomène physique. Les équations mathématiques qui composent ces modèles sont compliquées et ne peuvent pas être résolues analytiquement. Des procédures numériques doivent donc être employées pour obtenir des solutions approximatives à ces modèles. Ces procédures sont souvent des algorithmes itératifs, qui calculent une suite convergente vers la solution désirée à partir d’un essai initial. Ces méthodes sont une façon pratique et efficace de calculer des solutions à des systèmes complexes, et sont au coeur de la plupart des méthodes de simulation modernes. Dans cette thèse par article, nous présentons trois projets où les algorithmes itératifs jouent un rôle majeur dans une méthode de simulation ou de rendu. Premièrement, nous présentons une méthode pour améliorer la qualité visuelle de simulations fluides. En créant une surface de haute résolution autour d’une simulation existante, stabilisée par une méthode itérative, nous ajoutons des détails additionels à la simulation. Deuxièmement, nous décrivons une méthode de simulation fluide basée sur la réduction de modèle. En construisant une nouvelle base de champ de vecteurs pour représenter la vélocité d’un fluide, nous obtenons une méthode spécifiquement adaptée pour améliorer les composantes itératives de la simulation. Finalement, nous présentons un algorithme pour générer des images de haute qualité sur des écrans multicouches dans un contexte de réalité virtuelle. Présenter des images sur plusieurs couches demande des calculs additionels à coût élevé, mais nous formulons le problème de décomposition des images afin de le résoudre efficacement avec une méthode itérative simple.Realistic computer-generated images and simulations require complex models to properly capture the many subtle behaviors of each physical phenomenon. The mathematical equations underlying these models are complicated, and cannot be solved analytically. Numerical procedures must thus be used to obtain approximate solutions. These procedures are often iterative algorithms, where an initial guess is progressively improved to converge to a desired solution. Iterative methods are a convenient and efficient way to compute solutions to complex systems, and are at the core of most modern simulation methods. In this thesis by publication, we present three papers where iterative algorithms play a major role in a simulation or rendering method. First, we propose a method to improve the visual quality of fluid simulations. By creating a high-resolution surface representation around an input fluid simulation, stabilized with iterative methods, we introduce additional details atop of the simulation. Second, we describe a method to compute fluid simulations using model reduction. We design a novel vector field basis to represent fluid velocity, creating a method specifically tailored to improve all iterative components of the simulation. Finally, we present an algorithm to compute high-quality images for multifocal displays in a virtual reality context. Displaying images on multiple display layers incurs significant additional costs, but we formulate the image decomposition problem so as to allow an efficient solution using a simple iterative algorithm

IST Austria Thesis

Computer graphics is an extremely exciting field for two reasons. On the one hand, there is a healthy injection of pragmatism coming from the visual effects industry that want robust algorithms that work so they can produce results at an increasingly frantic pace. On the other hand, they must always try to push the envelope and achieve the impossible to wow their audiences in the next blockbuster, which means that the industry has not succumb to conservatism, and there is plenty of room to try out new and crazy ideas if there is a chance that it will pan into something useful. Water simulation has been in visual effects for decades, however it still remains extremely challenging because of its high computational cost and difficult artdirectability. The work in this thesis tries to address some of these difficulties. Specifically, we make the following three novel contributions to the state-of-the-art in water simulation for visual effects. First, we develop the first algorithm that can convert any sequence of closed surfaces in time into a moving triangle mesh. State-of-the-art methods at the time could only handle surfaces with fixed connectivity, but we are the first to be able to handle surfaces that merge and split apart. This is important for water simulation practitioners, because it allows them to convert splashy water surfaces extracted from particles or simulated using grid-based level sets into triangle meshes that can be either textured and enhanced with extra surface dynamics as a post-process. We also apply our algorithm to other phenomena that merge and split apart, such as morphs and noisy reconstructions of human performances. Second, we formulate a surface-based energy that measures the deviation of a water surface froma physically valid state. Such discrepancies arise when there is a mismatch in the degrees of freedom between the water surface and the underlying physics solver. This commonly happens when practitioners use a moving triangle mesh with a grid-based physics solver, or when high-resolution grid-based surfaces are combined with low-resolution physics. Following the direction of steepest descent on our surface-based energy, we can either smooth these artifacts or turn them into high-resolution waves by interpreting the energy as a physical potential. Third, we extend state-of-the-art techniques in non-reflecting boundaries to handle spatially and time-varying background flows. This allows a novel new workflow where practitioners can re-simulate part of an existing simulation, such as removing a solid obstacle, adding a new splash or locally changing the resolution. Such changes can easily lead to new waves in the re-simulated region that would reflect off of the new simulation boundary, effectively ruining the illusion of a seamless simulation boundary between the existing and new simulations. Our non-reflecting boundaries makes sure that such waves are absorbed

NEW ULTRA-LIGHTWEIGHT STIFF PANELS FOR SPACE APERTURES

Stiff, ultra-lightweight thermal-formed polyimide panels considered in this dissertation are examples of next generation gossamer structures that resolve some of the technology barriers of previous, membrane-dominated gossamer designs while maintaining their low mass and low stowage volume characteristics. The research involved statically and dynamically characterizing and modeling several of these panels to develop validated computer models which can be used to determine the effects of changing manufacturing parameters and scalability. Static characterization showed substantial local nonlinear behavior that was replicated by new physics-based finite element models, and global linear bending behavior that was modeled using classical shell finite elements incorporating effective properties in place of bulk material properties to represent the unique stiffening structure of these panels. Dynamic characterization was performed on individual panels using standard impact hammer and accelerometer testing, enabling successful extraction of several structural natural frequencies and mode shapes. Additionally, the three dimensional time history of the surface of the panels was rendered from video data, and temporal filters were applied to the data to examine the frequency content. These data were also correlated to the shell element numerical models. Overall, the research contributes to the total knowledge base of gossamer technologies, advances stiff panel-based structures toward space qualification, and demonstrates their potential for use in apertures and other spacecraft

Understanding the Structure of 3D Shapes

Compact representations of three dimensional objects are very often used in computer graphics to create effective ways to analyse, manipulate and transmit 3D models. Their ability to abstract from the concrete shapes and expose their structure is important in a number of applications, spanning from computer animation, to medicine, to physical simulations. This thesis will investigate new methods for the generation of compact shape representations. In the first part, the problem of computing optimal PolyCube base complexes will be considered. PolyCubes are orthogonal polyhedra used in computer graphics to map both surfaces and volumes. Their ability to resemble the original models and at the same time expose a very simple and regular structure is important in a number of applications, such as texture mapping, spline fitting and hex-meshing. The second part will focus on medial descriptors. In particular, two new algorithms for the generation of curve-skeletons will be presented. These methods are completely based on the visual appearance of the input, therefore they are independent from the type, number and quality of the primitives used to describe a shape, determining, thus, an advancement to the state of the art in the field