activity
20222026
most citedNeuralFMU: Presenting a workflow for integrating hybrid NeuralODEs into real world applications

1 citations · 1 across the 4 of their papers we have counts for

collaborators

9 papers

cs.HC2026

Assistive Robots and Reasonable Work Assignment Reduce Perceived Stigma toward Persons with Disabilities

Stina Klein, Birgit Prodinger, Elisabeth André +2

Robots are becoming more prominent in assisting persons with disabilities (PwD). Whilst there is broad consensus that robots can assist in mitigating physical impairments, the exte…

cs.CV2025

GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation

Phillip Mueller, Talip Uenlue, Sebastian Schmidt +4

Precise geometric control in image generation is essential for engineering \& product design and creative industries to control 3D object features accurately in image space. Tradit…

cs.HC2025

Holistic Specification of the Human Digital Twin: Stakeholders, Users, Functionalities, and Applications

Nils Mandischer, Alexander Atanasyan, Ulrich Dahmen +3

The digital twin of humans is a relatively new concept. While many diverse definitions, architectures, and applications exist, a clear picture is missing on what, in fact, makes a…

cs.HC2025

Conjugated Capabilities: Interrelations of Elementary Human Capabilities and Their Implication on Human-Machine Task Allocation and Capability Testing Procedures

Nils Mandischer, Larissa Füller, Torsten Alles +2

Human and automation capabilities are the foundation of every human-autonomy interaction and interaction pattern. Therefore, machines need to understand the capacity and performanc…

cs.RO2025

Beyond Features: How Dataset Design Influences Multi-Agent Trajectory Prediction Performance

Tobias Demmler, Jakob Häringer, Andreas Tamke +3

Accurate trajectory prediction is critical for safe autonomous navigation, yet the impact of dataset design on model performance remains understudied. This work systematically exam…

cs.LG2025

Masked Conditioning for Deep Generative Models

Phillip Mueller, Jannik Wiese, Sebastian Mueller +1

Datasets in engineering domains are often small, sparsely labeled, and contain numerical as well as categorical conditions. Additionally. computational resources are typically limi…