EG2025

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A no-API Approach to an Introductory Computer Graphics Course
(The Eurographics Association, 2025) Geigel, Joe; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
Many introductory computer graphics courses rely on the use of an established, GPU-inspired API to assist students in completing their programming assignments. However, as GPUs become more advanced, so too do the APIs used to interface with them, and as such, the complexity of using these modern APIs can sometimes overshadow the learning of basic graphics concepts that assignments are meant to illustrate. In this paper, we present an introductory course in computer graphics that takes an alternate approach whereby students do not make use of any API, instead creating their own rasterization engine written from scratch using an OpenGL-like shader-based architecture. We present the syllabus, course structure, and assignments for the course and share our observations on student learning from sections offered over the past several years.
Harnessing Artificial Intelligence to Expedite Content Creation for the Development of eXtended Reality Experiences
(The Eurographics Association, 2025) Freitas, André; Borges, João; Marques, Bernardo; Dias, Paulo; Santos, Beatriz Sousa; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
Despite eXtended Reality (XR) many benefits and demonstrated potential, the process of creating content specifically designed for distinct applications remains time-intensive and resource-demanding, hindering broader adoption. This study presents a student-driven project that investigates the role of Artificial Intelligence (AI) in streamlining the content creation process of 3D models. The proposed solution enables a user, equipped with an XR headset to use gesture recognition and perform a query via a text prompt, or as an alternative, to use voice recognition. Afterward, a request will be made to an API, which will generate the 3D model. Finally, the model will be added to a local library and become accessible in the XR environment, allowing users to manipulate, position, and other features. Initial findings highlight both opportunities and challenges, confirming it is already possible to integrate AI into a game engine with interesting results, while also showcasing that additional work is still necessary for obtaining more detailed and complex 3D models moving forward.
EUROGRAPHICS 2025: Education Papers Frontmatter
(The Eurographics Association, 2025) Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
Immersive Virtual Reality for Developing Spatial Skills in Learning 3D Transformations in Computer Graphics
(The Eurographics Association, 2025) Alobaid, Maha; Young, Gareth; Manzke, Michael; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
Learning computer graphics requires programming, problem-solving, mathematics, and spatial reasoning proficiency. One of the most challenging topics for students is understanding 3D transformations, a difficulty often attributed to insufficient spatial skills. Due to its immersive, interactive nature, virtual reality (VR) has been proposed as an effective tool for enhancing spatial reasoning. This study investigates how VR can support the learning of 3D transformations by developing an immersive virtual environment (IVE) designed to improve spatial visualization. The environment allows students to explore 3D objects within a coordinate system, interactively apply transformations, and visualize the relationship between transformation sequences and graphical API code. A mixed-methods study was conducted to evaluate the effectiveness of this VR-based approach. Quantitative results indicate a significant improvement in spatial ability. Qualitative feedback revealed that students found the VR environment engaging and intuitive. The ability to manipulate objects dynamically and observe transformations from multiple perspectives contributed to deeper conceptual understanding. These findings suggest that VR-based learning environments can enhance spatial skills and comprehension of 3D transformations in computer graphics education. Future work will explore long-term skill retention, instructional scaffolding, and alternative assessment methods to optimize VR-based learning.
Advancing XR Education: Towards a Multimodal Human-Machine Interaction Course for Doctoral Students in Computer Science
(The Eurographics Association, 2025) Silva, Samuel; Marques, Bernardo; Mendes, Daniel; Rodrigues, Rui; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
Nowadays, eXtended Reality (XR) has matured to the point where it seamlessly integrates various input and output modalities, enhancing the way users interact with digital environments. From traditional controllers and hand tracking to voice commands, eye tracking, and even biometric sensors, XR systems now offer more natural interactions. Similarly, output modalities have expanded beyond visual displays to include haptic feedback, spatial audio, and others, enriching the overall user experience. In this vein, as the field of XR becomes increasingly multimodal, the education process must also evolve to reflect these advancements. There is a growing need to incorporate additional modalities into the curriculum, helping students understand their relevance and practical applications. By exposing students to a diverse range of interaction techniques, they can better assess which modalities are most suitable for different contexts, enabling them to design more effective and human-centered solutions. This work describes an Advanced Human-Machine Interaction (HMI) course aimed at Doctoral Students in Computer Science. The primary objective is to provide students with the necessary knowledge in HMI by enabling them to articulate the fundamental concepts of the field, recognize and analyze the role of human factors, identify modern interaction methods and technologies, apply HCD principles to interactive system design and development, and implement appropriate methods for assessing interaction experiences across advanced HMI topics. In this vein, the course structure, the range of topics covered, assessment strategies, as well as the hardware and infrastructure employed are presented. Additionally, it highlights mini-projects, including flexibility for students to integrate their projects, fostering personalized and project-driven learning. The discussion reflects on the challenges inherent in keeping pace with this rapidly evolving field and emphasizes the importance of adapting to emerging trends. Finally, the paper outlines future directions and potential enhancements for the course.
Tracing Brilliance: Analysing Student Performance in Ray Tracing and Problem-Solving Capabilities and Approaches
(The Eurographics Association, 2025) Liu, Enyu; Wünsche, Burkhard C.; Luxton-Reilly, Andrew; Lange-Nawka, Dominik; Hooper, Steffan; Thompson, Samuel E. R.; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
Learning computer graphics is considered challenging due to the diverse skills required, including programming, mathematics, physics, problem solving skills, and spatial reasoning skills. Ray tracing is an important rendering technique in computer graphics but many students find the topic difficult. In this paper, we investigate problems students encounter when solving ray tracing questions by analyzing student answers to assessment questions for a third-year introductory Computer Graphics module. Our findings suggest that the difficulty of ray tracing questions is related to the challenge of integrating conceptual knowledge, programming skills, and mathematical concepts into problem-solving strategies. Our results provide insights how this effects students' problem solving capability, i.e., many students seem unable to make appropriate mental models of problem statements and hence give answers which violate fundamental properties of the problem statement. We also observed that many students solved problems through trial and error instead of identifying the cause of an error. We suggest that students might benefit from visualisation tools which help students making appropriate mental models.
Four years of sharing teaching practices within the French Computer Graphics community
(The Eurographics Association, 2025) Roudet, Céline; Belhadj, Farès; Sauvage, Basile; Maria, Maxime; Gilet, Guillaume; Bourdin, Jean-Jacques; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
This paper describes and provides feedback on a Computer Graphics (CG) teaching initiative conducted by the French Association of CG (AFIG in French), as part of its annual national conference (called j.FIG). The AFIG, historically focused primarily on research and doctoral education, has been leading the French academic community in CG for 30 years. Since the beginning of 2021, it has launched a working group dedicated to CG teaching in the Bachelor's and Master's cycles. Its main action was to present panels during the j.FIG, to address issues related to CG teaching on a national scale. This is analyzed in detail in this paper. For each of the four organized panels so far, we present its main goals and the underlying discussions and repercussions, by comparing them with similar state-of-the-art initiatives. Possible actions and proposals to perpetuate the event are finally discussed. More broadly, our ambition is to obtain reactions and provoke necessarily enriching discussions, enabling everyone to escape a little from the teacher's solitude, alone in front of his class.
Rendering Success - An Evaluation of Cheat Sheets for a Third-year Computer Graphics and Image Processing Course
(The Eurographics Association, 2025) Wünsche, Burkhard C.; Lange-Nawka, Dominik; Wang, Zixuan; Hooper, Steffan; Thompson, Samuel E. R.; Feng, Tony Haoran; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
Student-created cheat sheets, also called crib sheets, exam notes, or reference sheets, have been used to reduce exam anxiety, de-emphasise memorisation, and enable students to focus on high level learning. Previous work has identified properties of cheat sheets which correlate with exam performance and has shown conflicting results about the benefits of cheat sheets for different subjects. However, no such study has been conducted for more advanced courses requiring knowledge from different fields and different representations, such as a text, (API) code, formulas, and images. In this research we investigate which characteristics of student cheat sheets predict exam performance for a third-year Computer Graphics and Image Processing course. We analyse exam results with reference to the cheat sheets and questions of different levels of Bloom's taxonomy, and we discuss implications for students and instructors. We found that higher exam scores are correlated with a better coverage of course material and more formulas. Having many example questions with sample solutions and missing lecture topics was correlated with lower grades. We found a correlation between several of our metrics and performance in questions related to Bloom's ''Apply'' category. We suggest that students should be taught how to identify key lecture concepts, how to represent them (source code vs. formulas), and how to use them in exams.
Students Teaching Students Computer Art and Graphics
(The Eurographics Association, 2025) Wehar, Michael; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
We explore and reflect on how a multi-generational computer science capstone project has led three cohorts of students to learn basic concepts from undergraduate computer graphics. We investigate how our project model has led students learning computer graphics to create valuable educational tools and examples for future students to further learn computer graphics. This self-perpetuation of student development has led students to invest their time and energy into the education of themselves and that of future students. Moreover, we find that computer graphics is especially well suited for this educational model.
Interactive Particle System with Integrated Marching Cubes Algorithm
(The Eurographics Association, 2025) Tenckhoff, Sönke; Stauss, Simon; Neumann, Frank; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
This paper highlights a web-based platform that merges real-time particle simulations with Marching Cubes mesh generation, enabling fluid-like shapes to emerge from a dynamic emitter. The project emphasizes a modular architecture, ensuring flexible data flow and performance. A web-based development emphasizes accessibility and ease of use, differentiating it from traditional desktop-based visualization tools by enabling intuitive, immediate interaction directly within the browser. Key accomplishments include real-time parameter manipulation, immediate visual feedback and careful handling of computational complexity. Lessons learned range from optimizing efficiency under heavy particle loads to refining user experience.
Computer Graphics Instructors' Intentions for Using Generative AI for Teaching
(The Eurographics Association, 2025) Magana, Alejandra J.; Felkel, Petr; Žára, Jiří; Kuffner dos Anjos, Rafael; Rodriguez Echavarria, Karina
Background: Generative AI has significant potential to support learning processes, such as generating personalized content matching individual student needs. It also has the potential to support teaching processes by assisting instructors in generating content, assessing students, or supporting practice. This study investigates how computer graphics instructors have used generative AI or are planning to use generative AI to support their teaching. We implemented an anonymous online survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) methodology and distributed it among Eurographics members. The research questions were: (1) What are computer graphics instructors' ways of integrating generative AI for teaching and learning purposes? (2) What are the influencing factors computer graphics instructors have considered for integrating generative AI for teaching and learning purposes? Results: Between October 2024 and January 2025, we received 12 responses. Findings suggest that while some instructors have integrated generative AI into some aspects of their teaching, others have not and are hesitant to adopt them in the future, particularly as related to generating content for creating assignments such as lecture notes, summaries, teaching examples, etc., and supporting their assessment processes such as providing feedback, evaluating assignments, or grading exams. However, instructors were more open to using generative AI to support their teaching practices, particularly as related to pedagogy, such as providing students with interactive practice problems and supporting their creative content generation. Conclusion: Findings from the study identified the level of acceptance among computer graphics instructors, primarily full professors, and their experiences and intentions for using generative AI. To get a better understanding of the adoption of generative AI in the field of computer graphics education, we would like to invite the community to share their experiences and future intentions via the survey, which will remain open for additional input.
3D Shape Analysis: From Classical Optimisation Methods to Feature Learning for Shape Matching
(The Eurographics Association, 2025) Amrani, Nafie El; Lennart, Bastian; Ehm, Viktoria; Laehner, Zorah; Bernard, Florian; Mantiuk, Rafal; Hildebrandt, Klaus
The field of 3D shape analysis is concerned with the extraction of ''useful'' information from geometric data. Shape analysis has a high relevance for a wide range of applications, such as autonomous driving, biomedicine, or augmented/virtual reality. A core task of 3D shape analysis is shape matching, i.e. identifying correspondences between given shapes. While traditional shape matching methods rely on optimising a task-specific objective function, modern shape matching oftentimes involves datadriven components. We will first introduce traditional methods for shape matching, starting with the linear assignment problem and the quadratic assignment problem. We then present product graph formalisms in different settings, including 2D to 2D, 2D to 3D or shape to image, and 3D to 3D shape matching. We then discuss recent developments in learning-based shape correspondence methods, from learning shape correspondence with topological data structures to spectral approaches that provide efficient structure and circumvent annotations altogether. Furthermore, we discuss the practical relevance of these methods to application domains in image-to-image and shape-to-image correspondence, medical imaging and surgical navigation, and discuss how recent developments in foundation models play a role in shape analysis. Finally, the tutorial will conclude by addressing the challenges of shape matching, including handling partial shapes, and will explore potential future directions in the field.
EUROGRAPHICS 2025: Tutorials Frontmatter
(Eurographics Association, 2025) Mantiuk, Rafal; Hildebrandt, Klaus; Mantiuk, Rafal; Hildebrandt, Klaus
Next Generation 3D Face Models
(The Eurographics Association, 2025) Chandran, Prashanth; Mantiuk, Rafal; Hildebrandt, Klaus
Data driven 3D face models are an important tool for applications like facial animation, face reconstruction and tracking and can serve as a powerful prior for the complex nonrigid deformation of human faces. While linear 3D morphable models or 3DMMs have been traditionally employed by artists to cater to these applications, in the last few years several deep face models have been introduced that make use of neural networks to manipulate face shapes and offer greater flexibility while also retaining the intuitive control of traditional face models. This recent class of semantic deep face models have the potential to simplify existing facial animation workflows and enable artists to make a wider range of creative choices. However, as these neural tools are still very recent and fresh out of academic research, there is a need to start a conversation with artists and industry professionals on how such neural networks can be incorporated into existing workflows. This course aims to take a first step in this direction by providing a gentle introduction to several types of deep face models introduced in recent years by the academia and how each of them resolve several problems encountered in conventional facial animation. The primary intention of the course is to provide artists and industry professionals with an understanding of the state of art in neural 3D face models, and to inspire them to consider how these new tools can be incorporated into existing industry workflows to produce better content faster. The course will also serve the purpose of providing a gentle introduction to face modeling and animation to students looking to get familiar with the field. Experienced participants with a strong background in the field would also be able to identify possible directions for future research. The course will be presented in a lecture format with slides. Concepts from related papers will be explained in enough detail to help the audience make informed decisions on using these tools and understand their current shortcomings.
Traditional and Neural Order-Independent Transparency
(The Eurographics Association, 2025) Tsopouridis, Grigoris; Georgiou-Mousses, Christos; Fudos, Ioannis; Corrigan, David; Franke, Tobias Alexander; Mantiuk, Rafal; Hildebrandt, Klaus
Order independent transparency (OIT) is a technique in computer graphics that allows for accurate rendering of transparent objects without the need to sort them in a specific order based on their depth. Traditional transparency methods often suffer from artifacts and inaccuracies due to this sorting process, especially in complex scenes with many overlapping transparent surfaces. OIT is important because it provides a more visually correct representation of transparent materials, ensuring that colors mix accurately and that all elements are rendered consistently, regardless of their draw order. This enhances realism in applications such as video games, simulations, and visual effects in films. The tutorial will provide an overview of traditional (exact, approximate and hybrid) and deep learning approaches to OIT and examine their scope, performance and accuracy.
Virtual Humans meet Event-based and Quantum-enhanced Vision
(The Eurographics Association, 2025) Habermann, Marc; Golyanik, Vladislav; Mantiuk, Rafal; Hildebrandt, Klaus
The tutorial is split in two parts, i.e. two 90 minute talks. In the first half, Marc Habermann will provide an introduction to creating a digital twin of a real human. Second, Vladislav Golyanik will introduce new ways of sensing the real world using event-based vision and how quantum computers can enhance fundamental problems in graphics and vision.
Generating Color Schemes for your Digital Media & Data Visualizations
(The Eurographics Association, 2025) Rhyne, Theresa-Marie; Mantiuk, Rafal; Hildebrandt, Klaus
This tutorial provides an overview of the basics of color theory and shows how to use Generative AI tools, like OpenAI ChatGPT, Google Gemini, Microsoft Copilot and DeepSeek, to expand your data color scheme choices. You explore how to build your own colormaps by transforming color harmonies into data color schemes. This half day course is intended for a broad audience of individuals interested in understanding, applying, and building color schemes for data visualization.
Demystifying noise: The role of randomness in generative AI
(The Eurographics Association, 2025) Singh, Gurprit; Huang, Xingchang; Vandersanden, Jente; Oztireli, Cengiz; Mitra, Niloy; Mantiuk, Rafal; Hildebrandt, Klaus
This tutorial offers a thorough exploration of the role of randomness in generative AI, leveraging foundational knowledge from statistical physics, stochastic differential equations, and computer graphics. By connecting these disciplines, the tutorial aims to provide participants with a deep understanding of how noise impacts generative modeling and introduce state-of-the-art techniques and applications of noise in AI. First, we revisit the mathematical concepts essential for understanding diffusion and the integral role of noise in diffusion-based generative modeling. In the second part of the tutorial, we introduce the various types of noises studied within the computer graphics community and present their impact on rendering, texture synthesis and content creation. In the last part, we will look at how different noise correlations and noise schedulers impact the expressive power of image and video generation models. By the end of the tutorial, participants will gain an in-depth understanding of the mathematical constructs for diffusion models and how noise correlations can play an important role in enhancing the diversity and expressiveness of these models. The audience will also learn to code these noises developed in the graphics literature and their impact on generative modeling. The tutorial is aimed for students, researchers and practitioners, with our panel members bringing insights from the industry. All the materials related to the tutorial will be available on diffusion-noise.mpi-inf.mpg.de.
EUROGRAPHICS 2025: CGF 44-2 STARs Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2025) Memari, Pooran; Gryaditskaya, Yulia; Memari, Pooran; Gryaditskaya, Yulia
EUROGRAPHICS 2025: Posters Frontmatter
(Eurographics Association, 2025) Günther, Tobias; Montazeri, Zahra; Günther, Tobias; Montazeri, Zahra

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