EG2022

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https://diglib.eg.org/handle/10.2312/2633163

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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.
EUROGRAPHICS 2022: Tutorials Frontmatter
(The Eurographics Association, 2022) Hahmann, Stefanie; Patow, Gustavo A.; Hahmann, Stefanie; Patow, Gustavo A.
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, Eric
In 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.
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, Jasminka
We 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.
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.
Mesh Smoothing for Teaching GLSL Programming
(The Eurographics Association, 2022) Ilinkin, Ivaylo; Bourdin, Jean-Jacques; Paquette, Eric
This 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.
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, Giuseppe
Reinforcement 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.
Learning Generic Local Shape Properties for Adaptive Super-Sampling
(The Eurographics Association, 2022) Reinbold, Christian; Westermann, Rüdiger; Pelechano, Nuria; Vanderhaeghe, David
We 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.
Seamless Compressed Textures
(The Eurographics Association, 2022) Maggiordomo, Andrea; Tarini, Marco; Sauvage, Basile; Hasic-Telalovic, Jasminka
We 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.
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, Jasminka
We 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.
Multimodal Early Raw Data Fusion for Environment Sensing in Automotive Applications
(The Eurographics Association, 2022) Pederiva, Marcelo Eduardo; Martino, José Mario De; Zimmer, Alessandro; Sauvage, Basile; Hasic-Telalovic, Jasminka
Autonomous Vehicles became every day closer to becoming a reality in ground transportation. Computational advancement has enabled powerful methods to process large amounts of data required to drive on streets safely. The fusion of multiple sensors presented in the vehicle allows building accurate world models to improve autonomous vehicles' navigation. Among the current techniques, the fusion of LIDAR, RADAR, and Camera data by Neural Networks has shown significant improvement in object detection and geometry and dynamic behavior estimation. Main methods propose using parallel networks to fuse the sensors' measurement, increasing complexity and demand for computational resources. The fusion of the data using a single neural network is still an open question and the project's main focus. The aim is to develop a single neural network architecture to fuse the three types of sensors and evaluate and compare the resulting approach with multi-neural network proposals.
Graph Partitioning Algorithms for Rigid Body Simulations
(The Eurographics Association, 2022) Liu, Yinchu; Andrews, Sheldon; Pelechano, Nuria; Vanderhaeghe, David
We propose several graph partitioning algorithms for improving the performance of rigid body simulations. The algorithms operate on the graph formed by rigid bodies (nodes) and constraints (edges), producing non-overlapping and contiguous sub-systems that can be simulated in parallel by a domain decomposition technique. We demonstrate that certain partitioning algorithms reduce the computational time of the solver, and graph refinement techniques that reduce coupling between sub-systems, such as the Kernighan-Lin and Fiduccia-Mattheyses algorithms, give additional performance improvements.
Neural Denoising for Spectral Monte Carlo Rendering
(The Eurographics Association, 2022) Rouphael, Robin; Noizet, Mathieu; Prévost, Stéphanie; Deleau, Hervé; Steffenel, Luiz-Angelo; Lucas, Laurent; Sauvage, Basile; Hasic-Telalovic, Jasminka
Spectral Monte Carlo (MC) rendering is still to be largely adopted partially due to the specific noise, called color noise, induced by wavelength-dependent phenomenons. Motivated by the recent advances in Monte Carlo noise reduction using Deep Learning, we propose to apply the same approach to color noise. Our implementation and training managed to reconstruct a noise-free output while conserving high-frequency details despite a loss of contrast. To address this issue, we designed a three-step pipeline using the contribution of a secondary denoiser to obtain high-quality results.
Interactive Facial Expression Editing with Non-linear Blendshape Interpolation
(The Eurographics Association, 2022) Roh, Ji Hyun; Kim, Seong Uk; Jang, Hanyoung; Seol, Yeongho; Kim, Jongmin; Pelechano, Nuria; Vanderhaeghe, David
The ability to manipulate facial animations interactively is vital for enhancing the productivity and quality of character animation. In this paper, we present a novel interactive facial animation editing system that can express the naturalness of non-linear facial movements in real-time. The proposed system is based on a fully automatic algorithm that maintains all positional constraints while deforming the facial mesh as realistic as possible. Our method is based on direct manipulation with non-linear blendshape interpolation. We formulate the facial animation editing as a two-step quadratic minimization and solve it efficiently. From our results, the proposed method produces the desired and realistic facial animation better compared to existing mesh deformation methods, which are mainly based on linear combination and optimization.
Time Series AMR Data Representation for Out-of-core Interactive Visualization
(The Eurographics Association, 2022) Alexandre-Barff, Welcome; Deleau, Hervé; Sarton, Jonathan; Ledoux, Franck; Lucas, Laurent; Sauvage, Basile; Hasic-Telalovic, Jasminka
Time-varying Adaptive Mesh Refinement (AMR) data have become an essential representation for 3D numerical simulations in many scientific fields. This observation is even more relevant considering that the data volumetry has increased significantly, reaching petabytes, hence largely exceeding the memory capacities of the most recent graphics hardware. Therefore, the question is how to access these massive data - AMR time series in particular - for interactive visualization purposes, without cracks, artifacts or latency. In this paper, we present a time-varying AMR data representation to enable a possible fully GPU-based out-of-core approach. We propose to convert the input data initially expressed as regular voxel grids into a set of AMR bricks uniquely identified by a 3D Hilbert's curve and store them in mass storage.
From Capture to Immersive Viewing of 3D HDR Point Clouds
(The Eurographics Association, 2022) Loscos, Celine; Souchet, Philippe; Barrios, Théo; Valenzise, Giuseppe; Cozot, Rémi; Hahmann, Stefanie; Patow, Gustavo A.
The collaborators of the ReVeRY project address the design of a specific grid of cameras, a cost-efficient system that acquires at once several viewpoints, possibly under several exposures and the converting of multiview, multiexposed, video stream into a high quality 3D HDR point cloud. In the last two decades, industries and researchers proposed significant advances in media content acquisition systems in three main directions: increase of resolution and image quality with the new ultra-high-definition (UHD) standard; stereo capture for 3D content; and high-dynamic range (HDR) imaging. Compression, representation, and interoperability of these new media are active research fields in order to reduce data size and be perceptually accurate. The originality of the project is to address both HDR and depth through the entire pipeline. Creativity is enhanced by several tools, which answer challenges at the different stages of the pipeline: camera setup, data processing, capture visualisation, virtual camera controller, compression, perceptually guided immersive visualisation. It is the experience acquired by the researchers of the project that is exposed in this tutorial.
Geometric Deformation for Reducing Optic Flow and Cybersickness Dose Value in VR
(The Eurographics Association, 2022) Lou, Ruding; So, Richard H. Y.; Bechmann, Dominique; Sauvage, Basile; Hasic-Telalovic, Jasminka
Today virtual reality technologies is becoming more and more widespread and has found strong applications in various domains. However, the fear to experience motion sickness is still an important barrier for VR users. Instead of moving physically, VR users experience virtual locomotion but their vestibular systems do not sense the self-motion that are visually induced by immersive displays. The mismatch in visual and vestibular senses causes sickness. Previous solutions actively reduce user's field-of-view and alter their navigation. In this paper we propose a passive approach that temporarily deforms geometrically the virtual environment according to user navigation. Two deformation methods have been prototyped and tested. The first one reduces the perceived optic flow which is the main cause of visually induced motion sickness. The second one encourages users to adopt smoother trajectories and reduce the cybersickness dose value. Both methods have the potential to be applied generically.
State-of-the-Art in the Architecture, Methods and Applications of StyleGAN
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Bermano, Amit Haim; Gal, Rinon; Alaluf, Yuval; Mokady, Ron; Nitzan, Yotam; Tov, Omer; Patashnik, Or; Cohen-Or, Daniel; Meneveaux, Daniel; Patanè, Giuseppe
Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks. This state-of-the-art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations. It aims to be of use for both newcomers, who wish to get a grasp of the field, and for more experienced readers that might benefit from seeing current research trends and existing tools laid out. Among StyleGAN's most interesting aspects is its learned latent space. Despite being learned with no supervision, it is surprisingly well-behaved and remarkably disentangled. Combined with StyleGAN's visual quality, these properties gave rise to unparalleled editing capabilities. However, the control offered by StyleGAN is inherently limited to the generator's learned distribution, and can only be applied to images generated by StyleGAN itself. Seeking to bring StyleGAN's latent control to real-world scenarios, the study of GAN inversion and latent space embedding has quickly gained in popularity. Meanwhile, this same study has helped shed light on the inner workings and limitations of StyleGAN. We map out StyleGAN's impressive story through these investigations, and discuss the details that have made StyleGAN the go-to generator. We further elaborate on the visual priors StyleGAN constructs, and discuss their use in downstream discriminative tasks. Looking forward, we point out StyleGAN's limitations and speculate on current trends and promising directions for future research, such as task and target specific fine-tuning.
EUROGRAPHICS 2022: Short Papers Frontmatter
(The Eurographics Association, 2022) Pelechano, Nuria; Vanderhaeghe, David; Pelechano, Nuria; Vanderhaeghe, David
Virtual Ray Tracer
(The Eurographics Association, 2022) Verschoore de la Houssaije, Willard A.; Wezel, Chris S. van; Frey, Steffen; Kosinka, Jiri; Bourdin, Jean-Jacques; Paquette, Eric
Ray tracing is one of the more complicated techniques commonly taught in (introductory) Computer Graphics courses. Visualizations can help with understanding complex ray paths and interactions, but currently there are no openly accessible applications that focus on education. We present Virtual Ray Tracer, an interactive application that allows students/users to view and explore the ray tracing process in real-time. The application shows a scene containing a camera casting rays which interact with objects in the scene. Users are able to modify and explore ray properties such as their animation speed, the number of rays as well as the material properties of the objects in the scene. The goal of the application is to help the users-students of Computer Graphics and the general public-to better understand the ray tracing process and its characteristics. To invite users to learn and explore, various explanations and scenes are provided by the application at different levels of complexity. A user study showed the effectiveness of Virtual Ray Tracer in supporting the understanding and teaching of ray tracing. Our educational tool is built with the cross-platform engine Unity, and we make it fully available to be extended and/or adjusted to fit the requirements of courses at other institutions or of educational tutorials.

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