Parallel Rendering and Large Data Visualization

We are living in the big data age: An ever increasing amount of data is being produced through data acquisition and computer simulations. While large scale analysis and simulations have received significant attention for cloud and high-performance computing, software to efficiently visualise large data sets is struggling to keep up. Visualization has proven to be an efficient tool for understanding data, in particular visual analysis is a powerful tool to gain intuitive insight into the spatial structure and relations of 3D data sets. Large-scale visualization setups are becoming ever more affordable, and high-resolution tiled display walls are in reach even for small institutions. Virtual reality has arrived in the consumer space, making it accessible to a large audience. This thesis addresses these developments by advancing the field of parallel rendering. We formalise the design of system software for large data visualization through parallel rendering, provide a reference implementation of a parallel rendering framework, introduce novel algorithms to accelerate the rendering of large amounts of data, and validate this research and development with new applications for large data visualization. Applications built using our framework enable domain scientists and large data engineers to better extract meaning from their data, making it feasible to explore more data and enabling the use of high-fidelity visualization installations to see more detail of the data.Comment: PhD thesi

A network transparent, retained mode multimedia processing framework for the Linux operating system environment

Die Arbeit präsentiert ein Multimedia-Framework für Linux, das im Unterschied zu früheren Arbeiten auf den Ideen "retained-mode processing" und "lazy evaluation" basiert: Statt Transformationen unmittelbar auszuführen, wird eine abstrakte Repräsentation aller Medienelemente aufgebaut. "renderer"-Treiber fungieren als Übersetzer, die diese Darstellung zur Laufzeit in konkrete Operationen umsetzen, wobei das Datenmodell zahlreiche Optimierungen zur Reduktion der Anzahl der Schritte oder der Minimierung von Kommunikation erlaubt. Dies erlaubt ein stark vereinfachtes Programmiermodell bei gleichzeitiger Effizienzsteigerung. "renderer"-Treiber können zur Ausführung von Transformationen den lokalen Prozessor verwenden, oder können die Operationen delegieren. In der Arbeit wird eine Erweiterung des X Window Systems um Mechanismen zur Medienverarbeitung vorgestellt, sowie ein "renderer"-Treiber, der diese zur Delegation der Verarbeitung nutzt

Intuitive, Interactive Beard and Hair Synthesis with Generative Models

We present an interactive approach to synthesizing realistic variations in facial hair in images, ranging from subtle edits to existing hair to the addition of complex and challenging hair in images of clean-shaven subjects. To circumvent the tedious and computationally expensive tasks of modeling, rendering and compositing the 3D geometry of the target hairstyle using the traditional graphics pipeline, we employ a neural network pipeline that synthesizes realistic and detailed images of facial hair directly in the target image in under one second. The synthesis is controlled by simple and sparse guide strokes from the user defining the general structural and color properties of the target hairstyle. We qualitatively and quantitatively evaluate our chosen method compared to several alternative approaches. We show compelling interactive editing results with a prototype user interface that allows novice users to progressively refine the generated image to match their desired hairstyle, and demonstrate that our approach also allows for flexible and high-fidelity scalp hair synthesis.Comment: To be presented in the 2020 Conference on Computer Vision and Pattern Recognition (CVPR 2020, Oral Presentation). Supplementary video can be seen at: https://www.youtube.com/watch?v=v4qOtBATrv

Deep Image Matting: A Comprehensive Survey

Image matting refers to extracting precise alpha matte from natural images, and it plays a critical role in various downstream applications, such as image editing. Despite being an ill-posed problem, traditional methods have been trying to solve it for decades. The emergence of deep learning has revolutionized the field of image matting and given birth to multiple new techniques, including automatic, interactive, and referring image matting. This paper presents a comprehensive review of recent advancements in image matting in the era of deep learning. We focus on two fundamental sub-tasks: auxiliary input-based image matting, which involves user-defined input to predict the alpha matte, and automatic image matting, which generates results without any manual intervention. We systematically review the existing methods for these two tasks according to their task settings and network structures and provide a summary of their advantages and disadvantages. Furthermore, we introduce the commonly used image matting datasets and evaluate the performance of representative matting methods both quantitatively and qualitatively. Finally, we discuss relevant applications of image matting and highlight existing challenges and potential opportunities for future research. We also maintain a public repository to track the rapid development of deep image matting at https://github.com/JizhiziLi/matting-survey

Montage As A Participatory System: Interactions with the Moving Image

Full version unavailable due to 3rd party copyright restrictionsRecent developments in network culture suggest a weakening of hierarchical narratives of power and representation. Online technologies of distributed authorship appear to nurture a complex, speculative, contradictory and contingent realism. Yet there is a continuing deficit where the moving image is concerned, its very form appearing resistant to the dynamic throughputs and change models of real-time interaction. If the task is not to suspend but encourage disbelief as a condition in the user, how can this be approached as a design problem? In the attempt to build a series of design projects suggesting open architectures for the moving image, might a variety of (pre-digital) precursors from the worlds of art, architecture and film offer the designer models for inspiration or adaptation? A series of projects have been undertaken. Each investigates the composite moving image, specifically in the context of real-time computation and interaction. This arose from a desire to interrogate the qualia of the moving image within interactive systems, relative to a range of behaviours and/or observer positions, which attempt to situate users as conscious compositors. This is explored in the thesis through reflecting on a series of experimental interfaces designed for real time composition in performance, exhibition and online contexts