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Now showing 1 - 10 of 45
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.
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    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.