9 citations · 28 across the 23 of their papers we have counts for
4 papers · 1 filter
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…
MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific Generative Modeling
Damian Boborzi, Phillip Mueller, Jonas Emrich +3
Generative models have recently made remarkable progress in the field of 3D objects. However, their practical application in fields like engineering remains limited since they fail…
GeoBiked: A Dataset with Geometric Features and Automated Labeling Techniques to Enable Deep Generative Models in Engineering Design
Phillip Mueller, Sebastian Mueller, Lars Mikelsons
We provide a dataset for enabling Deep Generative Models (DGMs) in engineering design and propose methods to automate data labeling by utilizing large-scale foundation models. GeoB…
InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion Architecture
Phillip Mueller, Jannik Wiese, Ioan Craciun +1
Recent advancements in image synthesis are fueled by the advent of large-scale diffusion models. Yet, integrating realistic object visualizations seamlessly into new or existing ba…