EG 2025 - Tutorials

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    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.
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    EUROGRAPHICS 2025: Tutorials Frontmatter
    (Eurographics Association, 2025) Mantiuk, Rafal; Hildebrandt, Klaus; Mantiuk, Rafal; Hildebrandt, Klaus
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    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.
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    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.
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    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.
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    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.
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    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.