EG2024

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3D Reconstruction from Sketch with Hidden Lines by Two-Branch Diffusion Model
(The Eurographics Association, 2024) Fukushima, Yuta; Qi, Anran; Shen, I-Chao; Gryaditskaya, Yulia; Igarashi, Takeo; Hu, Ruizhen; Charalambous, Panayiotis
We present a method for sketch-based modelling of 3D man-made shapes that exploits not only the commonly considered visible surface lines but also the hidden lines typical for technical drawings. Hidden lines are used by artists and designers to communicate holistic shape structure. Given a single viewpoint sketch, leveraging such lines allows us to resolve the ambiguity of the shape's surfaces hidden from the observer. We assume that the separation into visible and hidden lines is given, and focus solely on how to leverage this information. Our strategy is to mingle two distinct diffusion networks: one generates denoized occupancy grid estimates from a visible line image, whilst the other generates occupancy grid estimates based on contextualized hidden lines unveiling the occluded shape structure. We iteratively merge noisy estimates from both models in a reverse diffusion process. Importantly, we demonstrate the importance of what we call a contextualized hidden lines image over just a hidden lines image. Our contextualized hidden lines image contains hidden lines and silhouette lines. Such contextualization allows us to achieve superior performance to a range of alternative configurations and reconstruct hidden holes and hidden surfaces.
Accurate Boundary Condition for Moving Least Squares Material Point Method using Augmented Grid Points
(The Eurographics Association, 2024) Toyota, Riku; Umetani, Nobuyuki; Hu, Ruizhen; Charalambous, Panayiotis
This paper introduces an accurate boundary-handling method for the moving least squares (MLS) material point method (MPM), which is a popular scheme for robustly simulating deformable objects and fluids using a hybrid of particle and grid representations coupled via MLS interpolation. Despite its versatility with different materials, traditional MPM suffers from undesirable artifacts around wall boundaries, for example, particles pass through the walls and accumulate. To address these issues, we present a technique inspired by a line handler for MLS-based image manipulation. Specifically, we augment the grid by adding points along the wall boundary to numerically compute the integration of the MLS weight. These additional points act as background grid points, improving the accuracy of the MLS interpolation around the boundary, albeit with a marginal increase in computational cost. In particular, our technique makes the velocity perpendicular to the wall nearly zero, preventing particles from passing through the wall. We compare the boundary behavior of 2D simulation against that of naïve approach.
Approaches to Nurturing Undergraduate Research in the Creative Industries - a UK Multi-Institutional Exploration
(The Eurographics Association, 2024) Anderson, Eike Falk; McLoughlin, Leigh; Gingrich, Oliver; Kanellos, Emmanouil; Adzhiev, Valery; Sousa Santos, Beatriz; Anderson, Eike
Undergraduate students aspiring to pursue careers in the creative industries, such as animation, video games, and computer art, require the ability to adapt and contribute to emerging and disruptive technologies. The cultivation of research skills fosters this adaptability and innovation, which is why research skills are considered important by employers. Promoting undergraduate research in computer graphics and related techniques is therefore necessary to ensure that students graduate not only with the vocational but also with the advanced research skills desired by the creative industries. This paper describes pedagogical approaches to nurturing undergraduate research across teaching, learning and through extracurricular activities - pioneered at three UK Higher Education Institutions. Providing observations, we are sharing educational strategies - reflecting on pedagogic experiences of supporting undergraduate research projects, many of which are practice-based. With this paper, we aim to contribute to a wider discussion around challenges and opportunities of student-led research.
Behavioral Landmarks: Inferring Interactions from Data
(The Eurographics Association, 2024) Lemonari, Marilena; Charalambous, Panayiotis; Panayiotou, Andreas; Chrysanthou, Yiorgos; Pettré, Julien; Liu, Lingjie; Averkiou, Melinos
We aim to unravel complex agent-environment interactions from trajectories, by explaining agent paths as combinations of predefined basic behaviors. We detect trajectory points signifying environment-driven behavior changes, ultimately disentangling interactions in space and time; our framework can be used for environment synthesis and authoring, shown by our case studies.
Bridging the Distance in Education: Design and Implementation of a Synchronous, Browser-Based VR Remote Teaching Tool
(The Eurographics Association, 2024) Pehlic, Abdulmelik; Augsdörfer, Ursula; Sousa Santos, Beatriz; Anderson, Eike
The rapid shift to remote education has presented numerous challenges for educators and students alike. Virtual Reality (VR) has emerged as a promising solution, offering immersive and interactive learning experiences. We design and implement a synchronous, browser-based VR teaching tool. The tool is compatible with budget VR equipment and enables meaningful engagement between teachers and students in a virtual setting, as well as active participation and interaction across a range of platforms, thus solving a range of disadvantages of current approaches.
Can GPT-4 Trace Rays
(The Eurographics Association, 2024) Feng, Tony Haoran; Wünsche, Burkhard C.; Denny, Paul; Luxton-Reilly, Andrew; Hooper, Steffan; Sousa Santos, Beatriz; Anderson, Eike
Ray Tracing is a fundamental concept often taught in introductory Computer Graphics courses, and Ray-Object Intersection questions are frequently used as practice for students, as they leverage various skills essential to learning Ray Tracing or Computer Graphics in general, such as geometry and spatial reasoning. Although these questions are useful in teaching practices, they may take some time and effort to produce, as the production procedure can be quite complex and requires careful verification and review. From the recent advancements in Artificial Intelligence, the possibility of automated or assisted exercise generation has emerged. Such applications are unexplored in Ray Tracing education, and if such applications are viable in this area, then it may significantly improve the productivity and efficiency of Computer Graphics instructors. Additionally, Ray Tracing is quite different to the mostly text-based tasks that LLMs have been observed to perform well on, hence it is unclear whether they can cope with these added complexities of Ray Tracing questions, such as visual processing and 3D geometry. Hence we ran some experiments to evaluate the usefulness of leveraging GPT-4 for assistance when creating exercises related to Ray Tracing, more specifically Ray-Object Intersection questions, and we found that an impressive 67% of its generated questions can be used in assessments verbatim, but only 42% of generated model solutions were correct.
Comparing NVIDIA RTX and a Novel Voxel-Space Ray Marching Approach as Global Illumination Solutions
(The Eurographics Association, 2024) Erlich, Oren; Aristizabal, Sarah; Li, Lucas; Woodard, Brandon; Humer, Irene; Eckhardt, Christian; Liu, Lingjie; Averkiou, Melinos
In this work, we investigate the performance-as well as the quality difference-between the state of the art NVIDIA DXR ray tracing pipeline and a voxelspace ray marching (VSRM). In order to maintain an acceptable quality image outcome, as well as frame-rate, for tested low numbers of rays from one to 32, we use a simple denoiser. We show a similar quality outcome and less progressive dependency on the number of rays for VSRM compared with DXR.
CS2023: An Update on the 2023 Computer Science Curricular Guidelines
(The Eurographics Association, 2024) Reiser, Susan L.; Sousa Santos, Beatriz; Anderson, Eike
In early 2024, the 2023 Computer Science Curricular Guidelines (CS2023) were endorsed by their sponsoring organizations: the Association of Computing Machinery (ACM), IEEE Computing Society, and the Association for the Advancement of Artificial Intelligence (AAAI). The CS2023 effort spanned four years and was the collaborative work of over 100 volunteers from six continents. The Eurographics' education community provided valuable feedback on the guidelines in its draft phase. In this session we would like to present a summary of the guidelines and seek feedback on its adoption and goal of being a living curriculum. The session is geared to anyone interested in computer science education. (see https://csed.acm.org/ cs2023-report-with-feedback/)
Cues to fast-forward collaboration: A Survey of Workspace Awareness and Visual Cues in XR Collaborative Systems
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Assaf, Rodrigo; Mendes, Daniel; Rodrigues, Rui; Aristidou, Andreas; Macdonnell, Rachel
Collaboration in extended reality (XR) environments presents complex challenges that revolve around how users perceive the presence, intentions, and actions of their collaborators. This paper delves into the intricate realm of group awareness, focusing specifically on workspace awareness and the innovative visual cues designed to enhance user comprehension. The research begins by identifying a spectrum of collaborative situations drawn from an analysis of XR prototypes in the existing literature. Then, we describe and introduce a novel classification for workspace awareness, along with an exploration of visual cues recently employed in research endeavors. Lastly, we present the key findings and shine a spotlight on promising yet unexplored topics. This work not only serves as a reference for experienced researchers seeking to inform the design of their own collaborative XR applications but also extends a welcoming hand to newcomers in this dynamic field.
DeepIron: Predicting Unwarped Garment Texture from a Single Image
(The Eurographics Association, 2024) Kwon, Hyun-Song; Lee, Sung-Hee; Hu, Ruizhen; Charalambous, Panayiotis
Realistic reconstruction of 3D clothing from an image has wide applications, such as avatar creation and virtual try-on. This paper presents a novel framework that reconstructs the texture map for 3D garments from a single garment image with pose. Since 3D garments are effectively modeled by stitching 2D garment sewing patterns, our specific goal is to generate a texture image for the sewing patterns. A key component of our framework, the Texture Unwarper, infers the original texture image from the input garment image, which exhibits warping and occlusion of the garment due to the user's body shape and pose. This is effectively achieved by translating between the input and output images by mapping the latent spaces of the two images. By inferring the unwarped original texture of the input garment, our method helps reconstruct 3D garment models that can show high-quality texture images realistically deformed for new poses. We validate the effectiveness of our approach through a comparison with other methods and ablation studies.
Dense 3D Gaussian Splatting Initialization for Sparse Image Data
(The Eurographics Association, 2024) Seibt, Simon; Chang, Thomas Vincent Siu-Lung; von Rymon Lipinski, Bartosz ; Latoschik, Marc Erich; Liu, Lingjie; Averkiou, Melinos
This paper presents advancements in novel-view synthesis with 3D Gaussian Splatting (3DGS) using a dense and accurate SfM point cloud initialization approach. We address the challenge of achieving photorealistic renderings from sparse image data, where basic 3DGS training may result in suboptimal convergence, thus leading to visual artifacts. The proposed method enhances precision and density of initially reconstructed point clouds by refining 3D positions and extrapolating additional points, even for difficult image regions, e.g. with repeating patterns and suboptimal visual coverage. Our contributions focus on improving ''Dense Feature Matching for Structure-from-Motion'' (DFM4SfM) based on a homographic decomposition of the image space to support 3DGS training: First, a grid-based feature detection method is introduced for DFM4SfM to ensure a welldistributed 3D Gaussian initialization uniformly over all depth planes. Second, the SfM feature matching is complemented by a geometric plausibility check, priming the homography estimation and thereby improving the initial placement of 3D Gaussians. Experimental results on the NeRF-LLFF dataset demonstrate that this approach achieves superior qualitative and quantitative results, even for fewer views, and the potential for a significantly accelerated 3DGS training with faster convergence.
Design and development of VR games for Cultural Heritage using Immersive Storytelling
(The Eurographics Association, 2024) Rizvic, Selma; Mijatovic, Bojan; Mania, Katerina; Artusi, Alessandro
In this tutorial we introduce the whole process of creating, designing and developing a serious VR game for cultural heritage using the concept of immersive storytelling. The use of serious games in education is allowing us to offer a different approach to learning. However, designing an application with gameplay parts as well as educational components in specific area such as cultural heritage can be challenging and different to many other methodologies in the creation of similar applications. The goal of the tutorial is to show different aspects of serious game creation and usage of our immersive storytelling methodology called hyper storytelling to help with the educational elements of the game. We will go through the creation of a story for the game; the creation of scenarios for the educational gameplay part; the filming of actors on green screen and filming of 360 videos; compositing of VR videos with actors and ambisonic sound; the creation of photogrammetry items; combining educational parts with gameplay and creating connection between them; application development and finalization of the product. At the end, we will showcase our example of the game.
Diffusion Models for Visual Content Generation
(The Eurographics Association, 2024) Mitra, Niloy; Mania, Katerina; Artusi, Alessandro
Diffusion models are now the state-of-the-art for producing images. These models have been trained on vast datasets and are increasingly repurposed for various image processing and conditional image generation tasks. We expect these models to be widely used in Computer Graphics and related research areas. Image generation has evolved into a rich promise of new possibilities, and in this tutorial, we will guide you through the intricacies of understanding and using diffusion models. This tutorial is targeted towards graphics researchers with an interest in image/video synthesis and manipulation. Attending the tutorial will enable participants to build a working knowledge of the core formulation, understand how to get started in this area, and study practical use cases to explore this new tool. Our goal is to get more researchers with expertise in computer graphics to start exploring the open challenges in this topic and explore innovative use cases in CG contexts in image synthesis and other media formats. From understanding the underlying principles to hands-on implementation, you'll gain practical skills that bridge theory and application. Throughout the tutorial, we'll explore techniques for generating lifelike textures, manipulating details, and achieving remarkable visual effects. By the end, you'll have a solid foundation in utilizing diffusion models for image generation, ready to embark on your creative projects. Join us as we navigate the fascinating intersection of computer graphics and diffusion models, where pixels become canvases and algorithms transform into brushes.
Distributed Surface Reconstruction
(The Eurographics Association, 2024) Marin, Diana; Komon, Patrick; Ohrhallinger, Stefan; Wimmer, Michael; Liu, Lingjie; Averkiou, Melinos
Recent advancements in scanning technologies and their rise in availability have shifted the focus from reconstructing surfaces from point clouds of small areas to large, e.g., city-wide scenes, containing massive amounts of data. We adapt a surface reconstruction method to work in a distributed fashion on a high-performance cluster, reconstructing datasets with millions of vertices in seconds. We exploit the locality of the connectivity required by the reconstruction algorithm to efficiently divide-andconquer the problem of creating triangulations from very large unstructured point clouds.
Driller: An Intuitive Interface for Designing Tangled and Nested Shapes
(The Eurographics Association, 2024) Butler, Tara; Guehl, Pascal; Parakkat, Amal Dev; Cani, Marie-Paule; Hu, Ruizhen; Charalambous, Panayiotis
The ability to represent not only isolated shapes but also shapes that interact is essential in various fields, from design to biology or anatomy. In this paper, we propose an intuitive interface to control and edit complex shape arrangements. Using a set of pre-defined shapes that may intersect, our ''Driller'' interface allows users to trigger their local deformation so that they rest on each other, become tangled, or even nest within each other. Driller provides an intuitive way to specify the relative depth of different shapes beneath user-selected points of interest by setting their local depth ordering perpendicularly to the camera's viewpoint. Deformations are then automatically generated by locally propagating these ordering constraints. In addition to being part of the final arrangement, some of the shapes can be used as deformers, which can be later deleted to help sculpt the target shapes. We implemented this solution within a sketch-based modeling system designed for novice users.
Efficient and Accurate Multi-Instance Point Cloud Registration with Iterative Main Cluster Detection
(The Eurographics Association, 2024) Yu, Zhiyuan; Zheng, Qin; Zhu, Chenyang; Xu, Kai; Hu, Ruizhen; Charalambous, Panayiotis
Multi-instance point cloud registration is the problem of recovering the poses of all instances of a model point cloud in a scene point cloud. A traditional solution first extracts correspondences and then clusters the correspondences into different instances. We propose an efficient and robust method which clusters the correspondences in an iterative manner. In each iteration, our method first computes the spatial compatibility matrix between the correspondences, and detects its main cluster. The main cluster indicates a potential occurrence of an instance, and we estimate the pose of this instance with the correspondences in the main cluster. Afterwards, the correspondences are removed to further register new instances in the following iterations. With this simplistic design, our method can adaptively determine the number of instances, achieving significant improvements on both efficiency and accuracy.
Emotional Responses to Exclusionary Behaviors in Intelligent Embodied Augmented Reality Agents
(The Eurographics Association, 2024) Apostolou, Kalliopi; Milata, Vaclav; Škola, Filip; Liarokapis, Fotis; Hu, Ruizhen; Charalambous, Panayiotis
This study investigated how interactions with intelligent agents, embodied as augmented reality (AR) avatars displaying exclusionary behaviors, affect users' emotions. Six participants engaged using voice interaction in a knowledge acquisition scenario in an AR environment with two ChatGPT-driven agents. The gaze-aware avatars, simulating realistic body language, progressively demonstrated social exclusion behaviors. Although not statistically significant, our data suggest a post-interaction emotional shift, manifested by decreased positive and negative affect-aligning with previous studies on social exclusion. Qualitative feedback revealed that some users attributed the exclusionary behavior of avatars to system glitches, leading to their disengagement. Our findings highlight challenges and opportunities for embodied intelligent agents, underscoring their potential to shape user experiences within AR, and the broader extended reality (XR) landscape.
EUROGRAPHICS 2024: CGF 43-2 STARs Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Aristidou, Andreas; Macdonnell, Rachel; Aristidou, Andreas; Macdonnell, Rachel
EUROGRAPHICS 2024: Education Papers Frontmatter
(The Eurographics Association, 2024) Sousa Santos, Beatriz; Anderson, Eike; Sousa Santos, Beatriz; Anderson, Eike
EUROGRAPHICS 2024: Posters Frontmatter
(Eurographics Association, 2024) Liu, Lingjie; Averkiou, Melinos; Liu, Lingjie; Averkiou, Melinos

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