EG 2018 - Tutorials
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Tutorials
Image-Based Information Visualization Tutorial
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Christophe Hurter
Understanding Mystery Behind Example-Based Image Synthesis
J. Lu, M. Lukác, and Daniel Sýkora
Voxel DAGs and Multiresolution Hierarchies: From Large-Scale Scenes to Pre-computed Shadows
Ulf Assarsson, Markus Billeter, Dan Dolonius, Elmar Eisemann, Alberto Jaspe, Leonardo
Scandolo, and Erik Sintorn
Deep Learning for Graphics
Niloy J. Mitra, Tobias Ritschel, Iasonas Kokkinos, Paul Guerrero, Vladimir Kim,
Konstantinos Rematas, and Ersin Yumer
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Item Image-Based Information Visualization Tutorial(The Eurographics Association, 2018) Hurter, Christophe; Ritschel, Tobias and Telea, AlexandruWhile many data exploration techniques are based on automatic knowledge extraction, other tools exist where the user plays the central role. This tutorial will report actual use-cases where the user interactively explores datasets and extracts relevant information. These techniques must be interactive enough to insure flexibility data exploration, therefore image-based algorithms propose a suitable solution. These algorithms, processed in parallel by the graphic card, are fast and scalable enough to support interactive big data exploration requirements.Item Understanding Mystery Behind Example-Based Image Synthesis(The Eurographics Association, 2018) Lu, J.; Lukác, M.; Sýkora, Daniel; Ritschel, Tobias and Telea, AlexandruThis tutorial presents a concise overview of development in the field of example-based image synthesis that over the last two decades rapidly evolved into a powerful tool enabling the production of synthetic imagery often indistinguishable from the source exemplar.We discuss not only the basic algorithmic concepts but also their further improvements which lead to significant reduction of computational overhead as well as better visual quality. We also demonstrate numerous applications including texture synthesis, hole-filling, video completion, retargeting, reshuffling, morphing, melding, painting by feature, appearance transfer to fluid animations or artistic style transfer to 3D models and facial animations.Item Voxel DAGs and Multiresolution Hierarchies: From Large-Scale Scenes to Pre-computed Shadows(The Eurographics Association, 2018) Assarsson, Ulf; Billeter, Markus; Dolonius, Dan; Eisemann, Elmar; Jaspe, Alberto; Scandolo, Leonardo; Sintorn, Erik; Ritschel, Tobias and Telea, AlexandruIn this tutorial, we discuss voxel DAGs and multiresolution hierarchies, which are representations that can encode large volumes of data very efficiently. Despite a significant compression ration, an advantage of these structures is that their content can be efficiently accessed in real-time. This property enables various applications. We begin the tutorial by introducing the concepts of sparsity and of coherency in voxel structures, and explain how a directed acyclic graph (DAG) can be used to represent voxel geometry in a form that exploits both aspects, while remaining usable in its compressed from for e.g. ray casting. In this context, we also discuss extensions that cover the time domain or consider an advanced encoding strategies exploiting symmetries and entropy. We then move on to voxel attributes, such as colors, and explain how to integrate such information with the voxel DAGs. We will provide implementation details and present methods for efficiently constructing the DAGs and also cover how to efficiently access the data structures with e.g. GPU-based ray tracers. The course will be rounded of with a segment on applications. We highlight a few examples and show their results. Pre-computed shadows are a special application, which will be covered in detail. In this context, we also explain how some of previous ideas contribute to multi-resolution hierarchies, which gives an outlook on the potential generality of the presented solutions.Item Deep Learning for Graphics(The Eurographics Association, 2018) Mitra, Niloy J.; Ritschel, Tobias; Kokkinos, Iasonas; Guerrero, Paul; Kim, Vladimir; Rematas, Konstantinos; Yumer, Ersin; Ritschel, Tobias and Telea, AlexandruIn computer graphics, many traditional problems are now better handled by deep-learning based data-driven methods. In applications that operate on regular 2D domains, like image processing and computational photography, deep networks are state-of-the-art, beating dedicated hand-crafted methods by significant margins. More recently, other domains such as geometry processing, animation, video processing, and physical simulations have benefited from deep learning methods as well. The massive volume of research that has emerged in just a few years is often difficult to grasp for researchers new to this area. This tutorial gives an organized overview of core theory, practice, and graphics-related applications of deep learning.Item EUROGRAPHICS 2018: Tutorials Frontmatter(Eurographics Association, 2018) Ritschel, Tobias; Telea, Alexandru; Ritschel, Tobias; Telea, Alexandru