EG2022

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

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2D Points Curve Reconstruction Survey and Benchmark
(The Eurographics Association, 2022) Ohrhallinger, Stefan; Peethambaran, Jiju; Parakkat, Amal Dev; Dey, Tamal K.; Muthuganapathy, R.; Hahmann, Stefanie; Patow, Gustavo A.
Curve reconstruction from unstructured points in a plane is a fundamental problem with many applications that has generated research interest for decades. Involved aspects like handling open, sharp, multiple and non-manifold outlines, run-time and provability as well as potential extension to 3D for surface reconstruction have led to many different algorithms. We survey the literature on 2D curve reconstruction and then present an open-sourced benchmark for the experimental study. Our unprecedented evaluation of a selected set of planar curve reconstruction algorithms aims to give an overview of both quantitative analysis and qualitative aspects for helping users to select the right algorithm for specific problems in the field. Our benchmark framework is available online to permit reproducing the results and easy integration of new algorithms.
3D Human Shape and Pose from a Single Depth Image with Deep Dense Correspondence Enabled Model Fitting
(The Eurographics Association, 2022) Wang, Xiaofang; Boukhayma, Adnane; Prévost, Stéphanie; Desjardin, Eric; Loscos, Celine; Multon, Franck; Sauvage, Basile; Hasic-Telalovic, Jasminka
We propose a two-stage hybrid method, with no initialization, for 3D human shape and pose estimation from a single depth image, combining the benefits of deep learning and optimization. First, a convolutional neural network predicts pixel-wise dense semantic correspondences to a template geometry, in the form of body part segmentation labels and normalized canonical geometry vertex coordinates. Using these two outputs, pixel-to-vertex correspondences are computed in a six-dimensional embedding of the template geometry through nearest neighbor. Second, a parametric shape model (SMPL) is fitted to the depth data by minimizing vertex distances to the input. Extensive evaluation on both real and synthetic human shape in motion datasets shows that our method yields quantitatively and qualitatively satisfactory results and state-of-the-art reconstruction errors.
Advances in Neural Rendering
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Tewari, Ayush; Thies, Justus; Mildenhall, Ben; Srinivasan, Pratul; Tretschk, Edith; Wang, Yifan; Lassner, Christoph; Sitzmann, Vincent; Martin-Brualla, Ricardo; Lombardi, Stephen; Simon, Tomas; Theobalt, Christian; Nießner, Matthias; Barron, Jon T.; Wetzstein, Gordon; Zollhöfer, Michael; Golyanik, Vladislav; Meneveaux, Daniel; Patanè, Giuseppe
Synthesizing photo-realistic images and videos is at the heart of computer graphics and has been the focus of decades of research. Traditionally, synthetic images of a scene are generated using rendering algorithms such as rasterization or ray tracing, which take specifically defined representations of geometry and material properties as input. Collectively, these inputs define the actual scene and what is rendered, and are referred to as the scene representation (where a scene consists of one or more objects). Example scene representations are triangle meshes with accompanied textures (e.g., created by an artist), point clouds (e.g., from a depth sensor), volumetric grids (e.g., from a CT scan), or implicit surface functions (e.g., truncated signed distance fields). The reconstruction of such a scene representation from observations using differentiable rendering losses is known as inverse graphics or inverse rendering. Neural rendering is closely related, and combines ideas from classical computer graphics and machine learning to create algorithms for synthesizing images from real-world observations. Neural rendering is a leap forward towards the goal of synthesizing photo-realistic image and video content. In recent years, we have seen immense progress in this field through hundreds of publications that show different ways to inject learnable components into the rendering pipeline. This state-of-the-art report on advances in neural rendering focuses on methods that combine classical rendering principles with learned 3D scene representations, often now referred to as neural scene representations. A key advantage of these methods is that they are 3D-consistent by design, enabling applications such as novel viewpoint synthesis of a captured scene. In addition to methods that handle static scenes, we cover neural scene representations for modeling nonrigidly deforming objects and scene editing and composition. While most of these approaches are scene-specific, we also discuss techniques that generalize across object classes and can be used for generative tasks. In addition to reviewing these state-ofthe- art methods, we provide an overview of fundamental concepts and definitions used in the current literature. We conclude with a discussion on open challenges and social implications.
Authoring Virtual Crowds: A Survey
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Lemonari, Marilena; Blanco, Rafael; Charalambous, Panayiotis; Pelechano, Nuria; Avraamides, Marios; Pettré, Julien; Chrysanthou, Yiorgos; Meneveaux, Daniel; Patanè, Giuseppe
Recent advancements in crowd simulation unravel a wide range of functionalities for virtual agents, delivering highly-realistic, natural virtual crowds. Such systems are of particular importance to a variety of applications in fields such as: entertainment (e.g., movies, computer games); architectural and urban planning; and simulations for sports and training. However, providing their capabilities to untrained users necessitates the development of authoring frameworks. Authoring virtual crowds is a complex and multi-level task, varying from assuming control and assisting users to realise their creative intents, to delivering intuitive and easy to use interfaces, facilitating such control. In this paper, we present a categorisation of the authorable crowd simulation components, ranging from high-level behaviours and path-planning to local movements, as well as animation and visualisation. We provide a review of the most relevant methods in each area, emphasising the amount and nature of influence that the users have over the final result. Moreover, we discuss the currently available authoring tools (e.g., graphical user interfaces, drag-and-drop), identifying the trends of early and recent work. Finally, we suggest promising directions for future research that mainly stem from the rise of learning-based methods, and the need for a unified authoring framework.
AvatarGo: Plug and Play self-avatars for VR
(The Eurographics Association, 2022) Ponton, Jose Luis; Monclús, Eva; Pelechano, Nuria; Pelechano, Nuria; Vanderhaeghe, David
The use of self-avatars in a VR application can enhance presence and embodiment which leads to a better user experience. In collaborative VR it also facilitates non-verbal communication. Currently it is possible to track a few body parts with cheap trackers and then apply IK methods to animate a character. However, the correspondence between trackers and avatar joints is typically fixed ad-hoc, which is enough to animate the avatar, but causes noticeable mismatches between the user's body pose and the avatar. In this paper we present a fast and easy to set up system to compute exact offset values, unique for each user, which leads to improvements in avatar movement. Our user study shows that the Sense of Embodiment increased significantly when using exact offsets as opposed to fixed ones. We also allowed the users to see a semitransparent avatar overlaid with their real body to objectively evaluate the quality of the avatar movement with our 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.
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.
CUDA and Applications to Task-based Programming
(The Eurographics Association, 2022) Kerbl, Bernhard; Kenzel, Michael; Winter, Martin; Steinberger, Markus; Hahmann, Stefanie; Patow, Gustavo A.
Since its inception, the CUDA programming model has been continuously evolving. Because the CUDA toolkit aims to consistently expose cutting-edge capabilities for general-purpose compute jobs to its users, the added features in each new version reflect the rapid changes that we observe in GPU architectures. Over the years, the changes in hardware, growing scope of built-in functions and libraries, as well as an advancing C++ standard compliance have expanded the design choices when coding for CUDA, and significantly altered the directives to achieve peak performance. In this tutorial, we give a thorough introduction to the CUDA toolkit, demonstrate how a contemporary application can benefit from recently introduced features and how they can be applied to task-based GPU scheduling in particular. For instance, we will provide detailed examples of use cases for independent thread scheduling, cooperative groups, and the CUDA standard library, libcu++, which are certain to become an integral part of clean coding for CUDA in the near future.
Digital Matte Painting - An Effective Undergraduate Assignment
(The Eurographics Association, 2022) Redford, Adam; Anderson, Eike Falk; Bourdin, Jean-Jacques; Paquette, Eric
This paper presents an effective digital matte painting assignment from a course delivered as part of an undergraduate degree programme in visual effects. The assignment involves the creation of a final 3D shot from an initial 2D image, using various 2D image manipulation tools and appropriate 2.5D image projection techniques.
EUROGRAPHICS 2022: CGF 41-2 STARs Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Meneveaux, Daniel; Patanè, Giuseppe; Meneveaux, Daniel; Patanè, Giuseppe
EUROGRAPHICS 2022: Education Papers Frontmatter
(The Eurographics Association, 2022) Bourdin, Jean-Jacques; Paquette, Eric; Bourdin, Jean-Jacques; Paquette, Eric
EUROGRAPHICS 2022: Posters Frontmatter
(The Eurographics Association, 2022) Sauvage, Basile; Hasic-Telalovic, Jasminka; Sauvage, Basile; Hasic-Telalovic, Jasminka
EUROGRAPHICS 2022: Short Papers Frontmatter
(The Eurographics Association, 2022) Pelechano, Nuria; Vanderhaeghe, David; Pelechano, Nuria; Vanderhaeghe, David
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.
Fast and Fine Disparity Reconstruction for Wide-baseline Camera Arrays with Deep Neural Networks
(The Eurographics Association, 2022) Barrios, Théo; Gerhards, Julien; Prévost, Stéphanie; Loscos, Celine; Sauvage, Basile; Hasic-Telalovic, Jasminka
Recently, disparity-based 3D reconstruction for stereo camera pairs and light field cameras have been greatly improved with the uprising of deep learning-based methods. However, only few of these approaches address wide-baseline camera arrays which require specific solutions. In this paper, we introduce a deep-learning based pipeline for multi-view disparity inference from images of a wide-baseline camera array. The network builds a low-resolution disparity map and retains the original resolution with an additional up scaling step. Our solution successfully answers to wide-baseline array configurations and infers disparity for full HD images at interactive times, while reducing quantification error compared to the state of the art.
A First Step Towards the Inference of Geological Topological Operations
(The Eurographics Association, 2022) Pascual, Romain; Belhaouari, Hakim; Arnould, Agnès; Le Gall, Pascale; Sauvage, Basile; Hasic-Telalovic, Jasminka
Procedural modeling enables building complex geometric objects and scenes in a wide panel of applications. The traditional approach relies on the sequential application of a reduced set of construction rules. We offer to automatically generate new topological rules based on an initial object and the expected result of the future operation. Non-expert users can thereby develop their own operations. We exploited our approach for the modeling of the geological subsoil.
Fitness of General-Purpose Monocular Depth Estimation Architectures for Transparent Structures
(The Eurographics Association, 2022) Wirth, Tristan; Jamili, Aria; Buelow, Max von; Knauthe, Volker; Guthe, Stefan; Pelechano, Nuria; Vanderhaeghe, David
Due to material properties, monocular depth estimation of transparent structures is inherently challenging. Recent advances leverage additional knowledge that is not available in all contexts, i.e., known shape or depth information from a sensor. General-purpose machine learning models, that do not utilize such additional knowledge, have not yet been explicitly evaluated regarding their performance on transparent structures. In this work, we show that these models show poor performance on the depth estimation of transparent structures. However, fine-tuning on suitable data sets, such as ClearGrasp, increases their estimation performance on the task at hand. Our evaluations show that high performance on general-purpose benchmarks translates well into performance on transparent objects after fine-tuning. Furthermore, our analysis suggests, that state-of-theart high-performing models are not able to capture a high grade of detail from both the image foreground and background at the same time. This finding shows the demand for a combination of existing models to further enhance depth estimation quality.
Fixed-radius Near Neighbors Searching for 2D Simulations on the GPU using Delaunay Triangulations
(The Eurographics Association, 2022) Porro, Heinich; Crespin, Benoît; Hitschfeld-Kahler, Nancy; Navarro, Cristobal; Sauvage, Basile; Hasic-Telalovic, Jasminka
We propose to explore a GPU solution to the fixed-radius nearest-neighbor problem in 2D based on Delaunay triangulations. This problem is crucial for many particle-based simulation techniques for collision detection or momentum exchange between particles. Our method computes the neighborhood of each particle at each iteration without neighbor lists or grids, using a Delaunay triangulation whose consistency is preserved by edge flipping. We study how this approach compares to a grid-based implementation on a flocking simulation with variable parameters.
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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