@misc{14070,
  abstract     = {{Deploying digital twins across the broader building stock is constrained by a key bottleneck: high-quality 3D content from LiDAR or photogrammetry remains labor-intensive and difficult to scale. We introduce Generative Twins (GT), a workflow that shifts digital twin creation from explicit geometric reconstruction toward probabilistic 3D synthesis. The pipeline takes a
single street-level façade photograph, corrects perspective distortion and removes occlusions, uses an instruction-conditioned multimodal image model to synthesise an isometric view of the building, and passes that view to an image-to-3D reconstruction model that leverages learned shape priors to produce a textured mesh without manual modelling. Three image to-3D platforms (Hitem3D, Hyper3D and Hunyuan3D 3.1) are compared on identical isometric inputs and assessed on architectural boundary clarity. We demonstrate GT through two applied cases: (i) reconstruction of ordinary residential streets in Detmold as stimuli for a virtual-reality study of multi sensory urban perception, and (ii) Green Editor, a design-exploration tool in which façade greenery is added or removed at the image layer before mesh generation.
Together these cases position Generative Twins as a scalable pathway for the digitization of ordinary building stock, with emphasis on controllability, editability, and iterative design workflows.}},
  author       = {{Ashmawy, Mohamed Khaled and Akay, Buse and Balderrama, Alvaro}},
  booktitle    = {{eCAADe proceedings}},
  issn         = {{2684-1843}},
  keywords     = {{Digital Twins, Virtual Reality (VR, Artificial Intelligence (AI), Image-to-3D, Generative Design.}},
  location     = {{Lübeck}},
  publisher    = {{eCAADe}},
  title        = {{{Towards Generative Twins: An AI Based Pipeline from Explicit 3D Reconstruction to Probabilistic Synthesis}}},
  doi          = {{10.52842/conf.ecaade.2026.1.619}},
  volume       = {{1}},
  year         = {{2026}},
}

@misc{9358,
  abstract     = {{Real-time human-centered assistance in industrial processes depends on the individual history of the work person’s activities in the work system and requires adequate methods for tracking the person’s actions. Most research in human activity recognition is based on recognizing actions from video data using computer vision methods. Digital equipment, standardized machine data interfaces, and smart wearable devices extend the possibilities to describe the current state of the work system. Petri nets have already been applied to human activity recognition, however, without the requirement of detecting actions in real-time. This paper proposes a Petri net architecture that enables hierarchical description-based human activity recognition in industrial work processes. We present an extension, a Partitioned Colored Petri Net, based on the colored Petri net formalism that infers activities from state transitions of the work system in real-time. In a case study, we demonstrate the Petri net’s application for an error-based learning system that visualizes error consequences in augmented reality using experimentable digital twins.}},
  author       = {{Herrmann, Jan-Phillip and Atanasyan, Alexander and Casser, Felix and Tackenberg, Sven}},
  booktitle    = {{Procedia Computer Science}},
  issn         = {{1877-0509}},
  keywords     = {{Colored Petri net, Human-centered Assistance, Experimentable Digital Twins}},
  location     = {{Österreich}},
  pages        = {{1188--199}},
  publisher    = {{Elsevier}},
  title        = {{{A Petri Net Architecture for Real-Time Human Activity Recognition in Work Systems}}},
  doi          = {{https://doi.org/10.1016/j.procs.2022.12.317}},
  volume       = {{217}},
  year         = {{2023}},
}