3 papers
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.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…
cs.CV2025
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…