Eldorado - Repositorium der TU Dortmund

Ressourcen aus und für Forschung, Lehre und Studium

Bei diesem Service handelt es sich um das Institutionelle Repositorium der Technischen Universität Dortmund. Hier werden Ressourcen aus und für Lehre, Studium und Forschung gespeichert, erschlossen und der Öffentlichkeit zugänglich gemacht.

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Aktuellste Veröffentlichungen

Die junge Bevölkerung von morgen
(2026-10) Olszenka, Ninja; Schößler, Sebastian
Wie viele Kinder werden in den kommenden Jahren in Deutschland leben? Insbesondere vor dem Hintergrund der unerwartet starken Rückgänge der Geburtenzahlen in den vergangenen Jahren gewinnt diese Frage aktuell an Bedeutung. Die Themenbroschüre nutzt die Ergebnisse der 16. koordinierten Bevölkerungsvorausberechnung des Statistischen Bundesamts, um die mögliche Bevölkerungsentwicklung in Ost- und Westdeutschland bis 2040 aufzuzeigen. Zunächst wird auf die Grundlagen der 16. kBV eingegangen und die zentralen Annahmen, auf denen die verschiedenen Varianten beruhen, erläutert. Anschließend wird der Fokus auf ausgewählte Varianten sowie auf die für die Kinder- und Jugendhilfe besonders relevanten Altersgruppen gelegt, um eine mögliche Spannweite der zukünftigen Bevölkerungsentwicklung abzubilden. Abschließend wird diskutiert, welche Bedeutung diese Entwicklungen für die verschiedenen Arbeitsfelder der Kinder- und Jugendhilfe haben könnten.
Amtliche Mitteilungen der Technischen Universität Dortmund Nr. 27/2026
(Technische Universität Dortmund, 2026-10-22)
Amtliche Mitteilungen der Technischen Universität Dortmund Nr. 26/2026
(Technische Universität Dortmund, 2026-10-21)
Leveraging data-based inductive biases in deep learning
(2025) Wagner, Stefan Sylvius; Harmeling, Stefan; Risse, Benjamin
Deep learning has revolutionised pattern recognition and decision-making, yet its success crucially depends on the inductive biases embedded in models implicit assumptions that guide learning toward useful solutions. These biases determine not only what a model can learn, but also how efficiently it can do so. Despite their importance, the sources and effects of inductive biases are rarely viewed in a more general manner. This dissertation investigates how inductive biases can be systematically identified and leveraged to improve learning across diverse domains. It formalises a distinction between two complementary kinds of inductive biases: (i) model-based inductive biases, which are encoded in architectures, loss functions, and regularization terms; and (ii) data-based inductive biases, which derive directly from structural or statistical regularities in the data. While the former has driven much of modern progress from convolutional networks to attention-based models, the latter remains comparatively underexplored despite its potential to capture task-specific invariances that are difficult to encode architecturally. The central contribution of this thesis is to define data-based inductive biases, which can be used to complement and enhance model-based inductive biases to create more robust and generalisable models. For this, a framework is introduced to describe how data-based inductive biases can be extracted and integrated into existing models, enhancing learning without the need for architectural redesign or extensive supervision. Latent variable modeling plays a key role in this framework, providing a principled means of discovering and encoding data structure as an additional learning signal. The chapters in this thesis demonstrate this principle across multiple domains. Cyclophobic Reinforcement Learning introduces a hand-crafted data-based inductive bias via hierarchical latent spaces over environment views, improving exploration by discouraging cyclic trajectories across these hierarchical perspectives. The Power of LLM-Generated Synthetic Data for Online Political Discussions uses synthetic data generated by large language models to define a controllable latent space, identifying ambiguous real-world samples that can be removed to reduce annotation cost or manually labelled to refine the model’s decision boundary. Just Cluster It: An Approach for Exploration in High Dimensions Using Clustering and Pre-Trained Representations shows how clustering DINO representations with Gaussian mixture models enables learning a latent density model over the state space, yielding more informative pseudo-count estimates for intrinsic motivation in 3D environments. Oh-A-DINO: Understanding and Enhancing Attribute-Level Information in Self-Supervised Object-Centric Representations demonstrates how augmenting DINOv2 representations with object-centric latent features learned through a Variational Autoencoder mitigates limitations of foundation models in representing non-geometric features such as colour and texture. Across these applications, the integration of data-derived structure consistently enhances performance and interpretability, showing that data-based inductive biases are a fundamental component to model learning.
Avatar scanning and personalization from consumer devices
(2026) Menzel, Timo; Botsch, Mario; Thies, Justus
This thesis presents methods for creating and personalizing realistic mesh-based avatars from smartphone input data. In the first part, we present an avatar reconstruction method that enables non-expert users to generate high-quality avatars using only consumer hardware, i.e., smartphones. To the best of our knowledge, this is the first fully automated end-to-end system that integrates guided smartphone capture with automated reconstruction to generate personalized, realistic, and VR-ready full-body avatars without requiring additional user input or preprocessing. Our reconstruction system consists of an iOS application that visually guides users through the scanning process and a server that automatically computes realistic avatars using photogrammetry, landmark detection, and template fitting. While this setup allows accessible avatar creation, the system is prone to motion-induced errors during scanning. We address this by reducing the effects of micro-motion at the photogrammetry stage and adapting the reconstruction method accordingly. Specifically, we compute partial point clouds from sub-scans using photogrammetry, incorporate silhouette constraints to improve fitting robustness, and apply inverse rendering to refine the texture on a per-image basis. The resulting avatars accurately match the scanned person's shape. However, animating the person's face is only possible with generic blendshapes. While this is sufficient to create new facial expressions, the resulting facial expression may lead to incongruent social cues. To address this, the second part of this thesis focuses on the personalization and animation of facial expressions. Building on the example-based facial rigging (EBFR) approach by Li et al. (2010), we introduce a method for personalizing blendshapes of realistic 3D avatars using smartphone input data. We developed an application to automatically capture example facial expressions. From these, we compute $52$ ARKit-compatible blendshapes using a position-based EBFR formulation and seamless partial deformation transfer. The resulting personalized blendshapes enable users to create faithful face animations. Finally, we evaluate the plausibility and naturalness of these facial animations by comparing different blendshape animation methods, including synthesized, tracked, and static (non-animated) facial expressions.