EG2023

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EUROGRAPHICS 2023: Tutorials Frontmatter
(Eurographics Association, 2023) Serrano, Ana; Slusallek, Philipp; Serrano, Ana; Slusallek, Philipp
Parameter-Free and Improved Connectivity for Point Clouds
(The Eurographics Association, 2023) Marin, Diana; Ohrhallinger, Stefan; Wimmer, Michael; Singh, Gurprit; Chu, Mengyu (Rachel)
Determining connectivity in unstructured point clouds is a long-standing problem that is still not addressed satisfactorily. In this poster, we propose an extension to the proximity graph introduced in [MOW22] to three-dimensional models. We use the spheres-of-influence (SIG) proximity graph restricted to the 3D Delaunay graph to compute connectivity between points. Our approach shows a better encoding of the connectivity in relation to the ground truth than the k-nearest neighborhood (kNN) for a wide range of k values, and additionally, it is parameter-free. Our result for this fundamental task offers potential for many applications relying on kNN, e.g., improvements in normal estimation, surface reconstruction, motion planning, simulations, and many more.
Using Vulkan for Graphics Research
(The Eurographics Association, 2023) Castorina, Marco; Sassone, Gabriel; Serrano, Ana; Slusallek, Philipp
The Vulkan API has has been released in 2016 and it has continued to evolve to include the latest hardware capabilities. Compared to OpenGL, it is a more verbose API that requires a deeper knowledge of the underlying hardware architecture. While this can make the API more difficult to get started with, it also rewards developers with finer grained control over resource management, multi-threading and work submission. This flexibility allows developers to achieve better performance over older APIs and opens the door to novel techniques that would have been harder, if not impossible, to implement before. In this tutorial we are going to provide an introduction to the core Vulkan API concepts and how they map to the underlying hardware. We are going to demonstrate how to leverage async compute to overlap graphics and compute work for better performance. We will provide detailed examples that make use of cutting-edge features like Mesh Shaders and Ray Tracing to achieve state of the art results in real-time rendering.
A Survey of Optimal Transport for Computer Graphics and Computer Vision
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Bonneel, Nicolas; Digne, Julie; Bousseau, Adrien; Theobalt, Christian
Optimal transport is a long-standing theory that has been studied in depth from both theoretical and numerical point of views. Starting from the 50s this theory has also found a lot of applications in operational research. Over the last 30 years it has spread to computer vision and computer graphics and is now becoming hard to ignore. Still, its mathematical complexity can make it difficult to comprehend, and as such, computer vision and computer graphics researchers may find it hard to follow recent developments in their field related to optimal transport. This survey first briefly introduces the theory of optimal transport in layman's terms as well as most common numerical techniques to solve it. More importantly, it presents applications of these numerical techniques to solve various computer graphics and vision related problems. This involves applications ranging from image processing, geometry processing, rendering, fluid simulation, to computational optics, and many more. It is aimed at computer graphics researchers desiring to follow optimal transport research in their field as well as optimal transport researchers willing to find applications for their numerical algorithms.
EUROGRAPHICS 2023: CGF 42-2 STARs Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Bousseau, Adrien; Theobalt, Christian; Bousseau, Adrien; Theobalt, Christian
Guiding Light Trees for Many-Light Direct Illumination
(The Eurographics Association, 2023) Hamann, Eric; Jung, Alisa; Dachsbacher, Carsten; Babaei, Vahid; Skouras, Melina
Path guiding techniques reduce the variance in path tracing by reusing knowledge from previous samples to build adaptive sampling distributions. The Practical Path Guiding (PPG) approach stores and iteratively refines an approximation of the incident radiance field in a spatio-directional data structure that allows sampling the incident radiance. However, due to the limited resolution in both spatial and directional dimensions, this discrete approximation is not able to accurately capture a large number of very small lights. We present an emitter sampling technique to guide next event estimation (NEE) with a global light tree and adaptive tree cuts that integrates into the PPG framework. In scenes with many lights our technique significantly reduces the RMSE compared to PPG with uniform NEE, while adding close to no overhead in scenes with few light sources. The results show that our technique can also aid the incident radiance learning of PPG in scenes with difficult visibility.
Quick-Pro-Build: A Web-based Approach for Quick Procedural 3D Reconstructions of Buildings
(The Eurographics Association, 2023) Bohlender, Bela; Mühlhäuser, Max; Guinea, Alejandro Sanchez; Babaei, Vahid; Skouras, Melina
We present Quick-Pro-Build, a web-based approach for quick procedural 3D reconstruction of buildings. Our approach allows users to quickly and easily create realistic 3D models using two integrated reference views: street view and satellite view. We introduce a novel conditional and stochastic shape grammar to represent the procedural models based on the well-established CGA shape grammar. Based on our grammar and user interface, we propose 3 modalities for procedural modeling: 1) model from scratch, 2) copy, paste, and adapt, and 3) summarize, select and adapt. The third modality enables users to model a building by summarizing similar models into an architectural style description, selecting a model from the style description, and adapting it to the target building. Summarizing and selecting allows the third modality to be the most efficient option when modeling a building with a style similar to existing buildings. The third modality is enabled by a novel algorithm that can find and combine similarities from procedural models into a style description and allows learning the preference of the users for one model inside the style description.
Efficient Needle Insertion Simulation using Hybrid Constraint Solver and Isolated DOFs
(The Eurographics Association, 2023) Martin, Claire; Zeng, Ziqiu; Courtecuisse, Hadrien; Babaei, Vahid; Skouras, Melina
This paper introduces a real-time compatible method to improve the location of constraints between a needle and tissues in the context of needle insertion simulation. This method is based on intersections between the Finite Element (FE) meshes of the needle and the tissues. It is coupled with the method of isolating mechanical DOFs and a hybrid solver (implying both direct and iterative resolutions) to respectively generate and solve the constraint problem while reducing the computation time.
Automatic Step Size Relaxation in Sphere Tracing
(The Eurographics Association, 2023) Bán, Róbert; Valasek, Gábor; Babaei, Vahid; Skouras, Melina
We propose a robust auto-relaxed sphere tracing method that automatically scales its step sizes based on data from previous iterations. It possesses a scalar hyperparemeter that is used similarly to the learning rate of gradient descent methods. We show empirically that this scalar degree of freedom has a smaller effect on performance than the step-scale hyperparameters of concurrent sphere tracing variants. Additionally, we compare the performance of our algorithm to these both on procedural and discrete signed distance input and show that it outperforms or performs up to par to the most efficient method, depending on the limit on iteration counts. We also verify that our method takes significantly fewer robustness-preserving sphere trace fallback steps, as it generates fewer invalid, over-relaxed step sizes.
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Nyatsanga, Simbarashe; Kucherenko, Taras; Ahuja, Chaitanya; Henter, Gustav Eje; Neff, Michael; Bousseau, Adrien; Theobalt, Christian
Gestures that accompany speech are an essential part of natural and efficient embodied human communication. The automatic generation of such co-speech gestures is a long-standing problem in computer animation and is considered an enabling technology for creating believable characters in film, games, and virtual social spaces, as well as for interaction with social robots. The problem is made challenging by the idiosyncratic and non-periodic nature of human co-speech gesture motion, and by the great diversity of communicative functions that gestures encompass. The field of gesture generation has seen surging interest in the last few years, owing to the emergence of more and larger datasets of human gesture motion, combined with strides in deep-learning-based generative models that benefit from the growing availability of data. This review article summarizes co-speech gesture generation research, with a particular focus on deep generative models. First, we articulate the theory describing human gesticulation and how it complements speech. Next, we briefly discuss rule-based and classical statistical gesture synthesis, before delving into deep learning approaches. We employ the choice of input modalities as an organizing principle, examining systems that generate gestures from audio, text and non-linguistic input. Concurrent with the exposition of deep learning approaches, we chronicle the evolution of the related training data sets in terms of size, diversity, motion quality, and collection method (e.g., optical motion capture or pose estimation from video). Finally, we identify key research challenges in gesture generation, including data availability and quality; producing human-like motion; grounding the gesture in the co-occurring speech in interaction with other speakers, and in the environment; performing gesture evaluation; and integration of gesture synthesis into applications. We highlight recent approaches to tackling the various key challenges, as well as the limitations of these approaches, and point toward areas of future development.
Synthetic Dataset for Panic Detection in Human Crowded Scenes
(The Eurographics Association, 2023) Calle, Javier; Leskovsky, Peter; Garcia, Jorge; Sanchez, Marti; Singh, Gurprit; Chu, Mengyu (Rachel)
AI is increasingly being used in public protection by using crowd anomaly detection. This is useful for identifying panic events enabling control forces to act faster. A significant challenge in this field is the lack of data for training these algorithms. Recreating panic events with big crowds can be both expensive and hazardous. To address this issue, this paper proposes the creation of a synthetic dataset for crowd panic behaviour. The process involves defining the scenario and setting up the appropriate CCTV cameras. Many scenarios are prepared, including variations in weather conditions. Next is the scene population with pedestrians and vehicles, with different crowd sizes and vehicle trajectories. To recreate panic, the behaviour of each person is programmed. The final videos show normality situations before the panic events start. Finally, we achieved 1717 simulations.
Tight Bounding Boxes for Voxels and Bricks in a Signed Distance Field Ray Tracer
(The Eurographics Association, 2023) Hansson-Söderlund, Herman; Akenine-Möller, Tomas; Babaei, Vahid; Skouras, Melina
We present simple methods to compute tight axis-aligned bounding boxes for voxels and for bricks of voxels in a signed distance function renderer based on ray tracing. Our results show total frame time reductions of 20-31% in a real-time path tracer.
Project Elements: A Computational Entity-component-system in a Scene-graph Pythonic Framework, for a Neural, Geometric Computer Graphics Curriculum
(The Eurographics Association, 2023) Papagiannakis, George; Kamarianakis, Manos; Protopsaltis, Antonis; Angelis, Dimitris; Zikas, Paul; Magana, Alejandra; Zara, Jiri
We present the Elements project, a lightweight, open-source, computational science and computer graphics (CG) framework, tailored for educational needs, that offers, for the first time, the advantages of an Entity-Component-System (ECS) along with the rapid prototyping convenience of a Scenegraph-based pythonic framework. This novelty allows advances in the teaching of CG: from heterogeneous directed acyclic graphs and depth-first traversals, to animation, skinning, geometric algebra and shader-based components rendered via unique systems all the way to their representation as graph neural networks for 3D scientific visualization. Taking advantage of the unique ECS in a a Scenegraph underlying system, this project aims to bridge CG curricula and modern game engines (MGEs), that are based on the same approach but often present these notions in a black-box approach. It is designed to actively utilize software design patterns, under an extensible open-source approach. Although Elements provides a modern (i.e., shader-based as opposed to fixed-function OpenGL), simple to program approach with Jupyter notebooks and unit-tests, its CG pipeline is not black-box, exposing for teaching for the first time unique challenging scientific, visual and neural computing concepts.
Game-based Transformations: A Playful Approach to Learning Transformations in Computer Graphics
(The Eurographics Association, 2023) Eisemann, Martin; Magana, Alejandra; Zara, Jiri
In this paper, we present a playful and game-based learning approach to teaching transformations in a second-year undergraduate computer graphics course. While the theoretical concepts were taught in class, the exercise consists of two web-based tools that help the students to get a playful grasp on the complex topic, which is the foundation for many of the later concepts typically taught in computer graphics, such as the rendering pipeline, animation, camera motion, shadow mapping and many more. The final students' projects and feedback indicate that the game-based introduction was well-received by the students.
Towards a Formal Education of Visual Effects Artists
(The Eurographics Association, 2023) Redford, Adam; Anderson, Eike Falk; Magana, Alejandra; Zara, Jiri
The rapid growth of the visual effects industry over the past three decades and increasing demand for high quality visual effects for film, television and similar media, in turn increasing demand for graduates in this field have highlighted the need for formal education in visual effects. In this paper, we explore the design of a visual effects undergraduate degree programme and discuss our aims and objectives in implementing this programme in terms of both curriculum and syllabus.
Neurosymbolic Models for Computer Graphics
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Ritchie, Daniel; Guerrero, Paul; Jones, R. Kenny; Mitra, Niloy J.; Schulz, Adriana; Willis, Karl D. D.; Wu, Jiajun; Bousseau, Adrien; Theobalt, Christian
Procedural models (i.e. symbolic programs that output visual data) are a historically-popular method for representing graphics content: vegetation, buildings, textures, etc. They offer many advantages: interpretable design parameters, stochastic variations, high-quality outputs, compact representation, and more. But they also have some limitations, such as the difficulty of authoring a procedural model from scratch. More recently, AI-based methods, and especially neural networks, have become popular for creating graphic content. These techniques allow users to directly specify desired properties of the artifact they want to create (via examples, constraints, or objectives), while a search, optimization, or learning algorithm takes care of the details. However, this ease of use comes at a cost, as it's often hard to interpret or manipulate these representations. In this state-of-the-art report, we summarize research on neurosymbolic models in computer graphics: methods that combine the strengths of both AI and symbolic programs to represent, generate, and manipulate visual data. We survey recent work applying these techniques to represent 2D shapes, 3D shapes, and materials & textures. Along the way, we situate each prior work in a unified design space for neurosymbolic models, which helps reveal underexplored areas and opportunities for future research.
PointCloudSlicer: Gesture-based Segmentation of Point Clouds
(The Eurographics Association, 2023) Gowtham, Hari Hara; Parakkat, Amal Dev; Cani, Marie-Paule; Babaei, Vahid; Skouras, Melina
Segmentation is a fundamental problem in point-cloud processing, addressing points classification into consistent regions, the criteria for consistency being based on the application. In this paper, we introduce a simple, interactive framework enabling the user to quickly segment a point cloud in a few cutting gestures in a perceptually consistent way. As the user perceives the limit of a shape part, they draw a simple separation stroke over the current 2D view. The point cloud is then segmented without needing any intermediate meshing step. Technically, we find an optimal, perceptually consistent cutting plane constrained by user stroke and use it for segmentation while automatically restricting the extent of the cut to the closest shape part from the current viewpoint. This enables users to effortlessly segment complex point clouds from an arbitrary viewpoint with the possibility of handling self-occlusions.
Towards L-System Captioning for Tree Reconstruction
(The Eurographics Association, 2023) Magnusson, Jannes S.; Hilsmann, Anna; Eisert, Peter; Babaei, Vahid; Skouras, Melina
This work proposes a novel concept for tree and plant reconstruction by directly inferring a Lindenmayer-System (L-System) word representation from image data in an image captioning approach. We train a model end-to-end which is able to translate given images into L-System words as a description of the displayed tree. To prove this concept, we demonstrate the applicability on 2D tree topologies. Transferred to real image data, this novel idea could lead to more efficient, accurate and semantically meaningful tree and plant reconstruction without using error-prone point cloud extraction, and other processes usually utilized in tree reconstruction. Furthermore, this approach bypasses the need for a predefined L-System grammar and enables species-specific L-System inference without biological knowledge.
Non-Separable Multi-Dimensional Network Flows for Visual Computing
(The Eurographics Association, 2023) Ehm, Viktoria; Cremers, Daniel; Bernard, Florian; Singh, Gurprit; Chu, Mengyu (Rachel)
Flows in networks (or graphs) play a significant role in numerous computer vision tasks. The scalar-valued edges in these graphs often lead to a loss of information and thereby to limitations in terms of expressiveness. For example, oftentimes highdimensional data (e.g. feature descriptors) are mapped to a single scalar value (e.g. the similarity between two feature descriptors). To overcome this limitation, we propose a novel formalism for non-separable multi-dimensional network flows. By doing so, we enable an automatic and adaptive feature selection strategy - since the flow is defined on a per-dimension basis, the maximizing flow automatically chooses the best matching feature dimensions. As a proof of concept, we apply our formalism to the multi-object tracking problem and demonstrate that our approach outperforms scalar formulations on the MOT16 benchmark in terms of robustness to noise.
Effective User Studies in Computer Graphics
(The Eurographics Association, 2023) Malpica, Sandra; Sun, Qi; Kellnhofer, Petr; Beacco, Alejandro; Senel, Gizem; McDonnell, Rachel; Flores Vargas, Mauricio; Serrano, Ana; Slusallek, Philipp
User studies are a useful tool for researchers, allowing them to collect data on how users perceive, interact with and process different types of sensory information. If planned in advance, user experiments can be leveraged in every stage of a research project, from early design, prototyping and feature exploration to applied proofs of concept, passing through validation and data collection for model training. User studies can provide the researcher with different types of information depending on the chosen methodology: user performance metrics, surveys and interviews, field studies, physiological data, etc. Considering human perception and other cognitive processes is particularly important in computer graphics, where most research produces outputs whose ultimate purpose is to be seen or perceived by a human. Being able to measure in an objective and systematic way how the information we generate is integrated into the representational space humans create to situate themselves in the world means that researchers will have more information to implement optimal algorithms, tools and techniques. In this tutorial we will give an overview of good practices for user studies in computer graphics with a particular focus on virtual reality use cases. We will cover the basics on how to design, carry out and analyze good user studies, as well as different particularities to be taken into account in immersive environments.

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