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Item Inverse Computational Spectral Geometry(The Eurographics Association, 2022) Rodolà, Emanuele; Cosmo, Luca; Ovsjanikov, Maks; Rampini, Arianna; Melzi, Simone; Bronstein, Michael; Marin, Riccardo; Hahmann, Stefanie; Patow, Gustavo A.In the last decades, geometry processing has attracted a growing interest thanks to the wide availability of new devices and software that make 3D digital data available and manipulable to everyone. Typical issues faced by geometry processing algorithms include the variety of discrete representations for 3D data (point clouds, polygonal or tet-meshes and voxels), or the type of deformation this data may undergo. Powerful approaches to address these issues come from looking at the spectral decomposition of canonical differential operators, such as the Laplacian, which provides a rich, informative, robust, and invariant representation of the 3D objects. The focus of this tutorial is on computational spectral geometry. We will offer a different perspective on spectral geometric techniques, supported by recent successful methods in the graphics and 3D vision communities and older but notoriously overlooked results. We will discuss both the “forward” path typical of spectral geometry pipelines (e.g. computing Laplacian eigenvalues and eigenvectors of a given shape) with its widespread applicative relevance, and the inverse path (e.g. recovering a shape from given Laplacian eigenvalues, like in the classical “hearing the shape of the drum” problem) with its ill-posed nature and the benefits showcased on several challenging tasks in graphics and geometry processing.Item EUROGRAPHICS 2022: Tutorials Frontmatter(The Eurographics Association, 2022) Hahmann, Stefanie; Patow, Gustavo A.; Hahmann, Stefanie; Patow, Gustavo A.Item Evaluating Bloom's Taxonomy-based Learning Modules for Parallel Coordinates Literacy(The Eurographics Association, 2022) Peng, Ilena; Firat, Elif E.; Laramee, Robert S.; Joshi, Alark; Bourdin, Jean-Jacques; Paquette, EricIn this paper, we present the results of an intervention designed to introduce parallel coordinates to students. The intervention contains six new modules inspired by Bloom's taxonomy that featured a combination of videos, tests, and tasks. We studied the impact of our modules with a corrective feedback mechanism inspired by Mastery Learning. Based on analyzing the data of our students, we found that students in the Corrective Immediate Feedback (CIF) group performed better on average on all the modules as compared to the students in the No Feedback (NF) group. In the tasks where students were required to construct parallel coordinates plots, students in the Corrective Immediate Feedback group produced plots with appropriate use of color, labels, legends, etc. Overall, students in both groups grew more confident in their ability to recognize parallel coordinates plots and expressed high confidence in their ability to interpret, create, and use parallel coordinates plots for data exploration and presentation in the future.Item View Dependent Decompression for Web-based Massive Triangle Meshes Visualisation(The Eurographics Association, 2022) Cecchin, Alice; Du, Paul; Pastor, Mickaël; Agouzoul, Asma; Sauvage, Basile; Hasic-Telalovic, JasminkaWe introduce a framework extending an existing progressive compression-decompression algorithm for 3D triangular meshes. First, a mesh is partitioned. Each resulting part is compressed, then joined with one of its neighbours. These steps are repeated following a binary tree of operations, until a single compressed mesh remains. Decompressing the mesh involves progressively performing those steps in reverse, per node, and locally, by selecting the branch of the tree to explore. This method creates a compact and lossless representation of the model that allows its progressive and local reconstruction. Previously unprocessable meshes can be visualized on the web and mobile devices using this technique.Item Computational Assemblies: Analysis, Design, and Fabrication(The Eurographics Association, 2022) Song, Peng; Wang, Ziqi; Livesu, Marco; Hahmann, Stefanie; Patow, Gustavo A.Assemblies are ubiquitous in our daily life, such as toys, electronic devices, furniture, and architecture. They enable to build large and complex objects by composing small yet simpler parts, facilitating fabrication, storage, maintenance, and usage. However, designing assemblies is a highly non-trivial task because one needs to consider not only the properties of each individual components, but also of the whole assembly, such as aesthetics and stability. Motivated by recent advancements in digital fabrication, various computational techniques have been developed to analyze, design, and fabricate assemblies, aiming to enable general users to easily personalize them. This tutorial will give an introduction to these computational techniques, focusing on four fundamental aspects, i.e., parts fabricability, parts joining, assembly planning, and structural stability. In this tutorial, we will take a deep dive into computational methods to analyze these aspects for a given assembly as well as to design and fabricate assemblies that satisfy user-specified requirements in these aspects. This tutorial assumes knowledge of the fundamentals of computer graphics. Attendees should come away from this tutorial with a broad understanding of current work in computational assemblies, as well as familiarity with the necessary knowledge to start their own research in this area.Item Mesh Smoothing for Teaching GLSL Programming(The Eurographics Association, 2022) Ilinkin, Ivaylo; Bourdin, Jean-Jacques; Paquette, EricThis paper shares ideas for effective assignment that can be used to introduce a number of advanced GLSL concepts including shader storage buffer objects, transform feedback, and compute shaders. The assignment is based on published research on mesh smoothing which serves as a motivating factor and offers a sense of accomplishment.Item A Survey on Reinforcement Learning Methods in Character Animation(The Eurographics Association and John Wiley & Sons Ltd., 2022) Kwiatkowski, Ariel; Alvarado, Eduardo; Kalogeiton, Vicky; Liu, C. Karen; Pettré, Julien; Panne, Michiel van de; Cani, Marie-Paule; Meneveaux, Daniel; Patanè, GiuseppeReinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment. While learning, they repeatedly take actions based on their observation of the environment, and receive appropriate rewards which define the objective. This experience is then used to progressively improve the policy controlling the agent's behavior, typically represented by a neural network. This trained module can then be reused for similar problems, which makes this approach promising for the animation of autonomous, yet reactive characters in simulators, video games or virtual reality environments. This paper surveys the modern Deep Reinforcement Learning methods and discusses their possible applications in Character Animation, from skeletal control of a single, physically-based character to navigation controllers for individual agents and virtual crowds. It also describes the practical side of training DRL systems, comparing the different frameworks available to build such agents.Item Learning Generic Local Shape Properties for Adaptive Super-Sampling(The Eurographics Association, 2022) Reinbold, Christian; Westermann, Rüdiger; Pelechano, Nuria; Vanderhaeghe, DavidWe propose a novel encoder/decoder-based neural network architecture that learns view-dependent shape and appearance of geometry represented by voxel representations. Since the network is trained on local geometry patches, it generalizes to arbitrary models. A geometry model is first encoded into a sparse voxel octree of features learned by a network, and this model representation can then be decoded by another network in-turn for the intended task. We utilize the network for adaptive supersampling in ray-tracing, to predict super-sampling patterns when seeing coarse-scale geometry. We discuss and evaluate the proposed network design, and demonstrate that the decoder network is compact and can be integrated seamlessly into on-chip ray-tracing kernels. We compare the results to previous screen-space super-sampling strategies as well as non-network-based world-space approaches.Item Seamless Compressed Textures(The Eurographics Association, 2022) Maggiordomo, Andrea; Tarini, Marco; Sauvage, Basile; Hasic-Telalovic, JasminkaWe present an algorithm to hide discontinuity artifacts at seams in GPU compressed textures. Texture mapping requires UV-maps, and UV-maps (in general) require texture seams; texture seams (in general) cause small visual artifacts in rendering; these can be prevented by careful, slight modifications a few texels around the seam. Unfortunately, GPU-based texture compression schemes are lossy and introduce their own slight modifications of texture values, nullifying that effort. The result is that texture compression may reintroduce the visual artefacts at seams. We modify a standard texture compression algorithm to make it aware of texture seams, resulting in compressed textures that still prevent the seam artefacts.Item Consistent Multi- and Single-View HDR-Image Reconstruction from Single Exposures(The Eurographics Association, 2022) Mohan, Aditya; Zhang, Jing; Cozot, Rémi; Loscos, Celine; Sauvage, Basile; Hasic-Telalovic, JasminkaWe propose a CNN-based approach for reconstructing HDR images from just a single exposure. It predicts the saturated areas of LDR images and then blends the linearized input with the predicted outputs. Two loss functions are used: the Mean Absolute Error and the Multi-Scale Structural Similarity Index. The choice of these loss functions allows us to outperform previous algorithms in the reconstructed dynamic range. Once the network trained, we input multi-view images to it to output multi-view coherent images.