AR Applied to the Tactile Models. Museo di Arte Orientale in Turin: Communicating the Vaulted System of Palazzo Mazzonis
In recent decades, museums, as fundamental cultural institutions of modern society, have increasingly explored innovative methods and tools for the communication and dissemination of knowledge. This process has led to significant changes in their approaches to visitor engagement. One of the most common strategies for creating interactive content in the museum field is the use of extended reality (XR) technologies and direct haptic experiences that support storytelling and interaction. By combining virtual and real environments, XR can stimulate multiple senses through the addition of digital inputs, thereby expanding the channels of knowledge transmission.
In the museum sector, however, there is still a lack of widespread and shared awareness regarding how virtual and real content should interact and how the most effective experiences for cultural transmission should be designed. The presented case study focuses on the vaulted system of the atrium of Palazzo Mazzonis in Turin, the seat of the Museum of Oriental Art, where a structured and well-tested methodology of geometric decomposition and digital reconstruction has been applied.
Augmented Reality (AR) and a digitally fabricated 3D model have been used to communicate the results of this study to a broad audience, serving different purposes ranging from research and dissemination to tourism and recreational activities.
Markerless AR Applications and 3D Printing for the Augmented Prototyping of the Franciscan Heritage of the XVIII Century
The Wooden Tabernacles research project, which began in September 2022 and is still ongoing, concerns the digitization of a series of wooden tabernacles produced between the seventeenth and eighteenth centuries as historical and artistic testimonies of the Capuchin order in Abruzzo.
The aim of the project is to obtain, through SfM photogrammetry, a series of geometrically accurate 3D models that can be used to create physical replicas of the surveyed objects through 3D printing technologies.
The research project also includes the implementation of an Augmented Reality (AR) application for the visualization of the mapped 3D models and other queryable information. As is well known, Augmented Reality allows digital elements to be superimposed onto a real scene; this feature generates an enhanced perception of reality by applying, through the use of specific devices, a digital information layer that modifies the real scene in order to deepen knowledge and understanding. By combining this technology with 3D printing, it becomes possible to associate a physical object with a series of informational contents that enrich its fruition and accessibility.
The Augmented Reality application will make it possible to expand the experience of using the replica by adding a series of digital information layers (photos, videos, and 3D models) that can be activated by framing the replica itself. This application has already been developed by implementing the Model Target functionalities available through the Vuforia libraries within the Unity software environment.
The 3D-printed prototype provides a physical reproduction of the object at a 1:5 scale, capable of accurately reproducing the richness of detail of the original artifact.
The paper investigates the evolution of AI-assisted architectural representation from early unpredictable text-to-image generation systems toward more controlled and precise workflows based on Stable Diffusion, ControlNet, and LookX AI. The research analyzes the role of latent spaces, diffusion models, GANs, convolutional neural networks, and AI rendering systems in architectural visualization, emphasizing the transition from exploratory AI image production to controllable design-oriented generation. Through experimental workflows combining Rhinoceros 3D models, ControlNet preprocessors, segmentation maps, depth maps, edge detection, and prompt engineering, the study evaluates how AI systems can support architectural rendering, stylistic control, spatial coherence, and atmosphere generation. The paper compares open-source and cloud-based AI platforms, discussing the balance between creativity, predictability, customization, and architectural precision in contemporary AI-assisted design workflows.
The paper presents a Virtual Reality-based feedback system designed to support product design evaluation and customer interaction during the development process. The research proposes a Product-Service System (PSS) workflow integrating VR environments, game engines, 3D modeling, usability testing, and interactive feedback collection to improve communication between designers and users during the early design stages. The methodology combines Autodesk 3DS Max, Unreal Engine, Unity3D, C# scripting, and web-based VR deployment to create immersive environments in which users can interact with virtual products, manipulate materials and geometries, and provide evaluative feedback through integrated usability interfaces. The system is tested through interactive prototypes of industrial design objects, demonstrating how VR and real-time 3D visualization can support iterative product refinement, customer-centered design, and collaborative decision-making within digital product development pipelines.
The New A.I.: Gaining Control Over the Noise
The paper investigates the evolution of AI-assisted architectural representation from early unpredictable text-to-image generation systems toward more controlled and precise workflows based on Stable Diffusion, ControlNet, and LookX AI. The research analyzes the role of latent spaces, diffusion models, GANs, convolutional neural networks, and AI rendering systems in architectural visualization, emphasizing the transition from exploratory AI image production to controllable design-oriented generation. Through experimental workflows combining Rhinoceros 3D models, ControlNet preprocessors, segmentation maps, depth maps, edge detection, and prompt engineering, the study evaluates how AI systems can support architectural rendering, stylistic control, spatial coherence, and atmosphere generation. The paper compares open-source and cloud-based AI platforms, discussing the balance between creativity, predictability, customization, and architectural precision in contemporary AI-assisted design workflows.
AI Text-To-Image Procedure for the Visualization of Figurative and Literary Tòpoi
The paper proposes a workflow for translating literary and figurative textual descriptions into AI-generated images through text-to-image neural networks. The research combines linguistic analysis, lexical semantics, prompt engineering, and Stable Diffusion-based image generation to investigate the relationship between verbal and visual representation. Drawing on theories of visual culture, ekphrasis, and Aby Warburg’s Mnemosyne Atlas, the study develops a methodological framework for guiding neural networks through semantic keywords, syntactic structures, contextual references, and prompt modulation. The workflow is tested on literary and architectural texts from different historical periods, including utopian cities, nineteenth-century urban descriptions, and imaginary urban narratives. The research demonstrates how AI image generation can support the visualization of literary spatial imaginaries while also revealing the ambiguities, arbitrariness, and interpretative challenges inherent in translating text into visual form.
Floating Acrobats: Exploring Exaptation in Architecture Through Artificial Intelligence
The paper explores the intersection between generative artificial intelligence, virtual reality, performance art, and architectural experimentation through the practice-based installation Floating Acrobats. The research investigates the concept of exaptation as a design strategy, examining how AI-generated imagery, VR environments, sculptural design, and performative spatial experiences can be repurposed into new artistic and architectural functions. The methodology combines hand sketches, 3D modeling, AI-generated visual simulations, immersive VR environments, and interactive exhibition design to create a multisensory installation centered on resilience, adaptability, and collective creativity. The study reflects on the transformative role of generative AI in artistic production, virtual exhibition spaces, authorship, and morphospatial experimentation, proposing AI as a catalyst for new forms of architectural imagination and performative interaction.
Is a Picture Worth a Thousand Words? Comparative Evaluation of Generative AI for Drawing and Representation
The paper presents a comparative evaluation of generative AI systems for drawing and architectural representation, focusing on the operational principles, workflows, and visual outputs of text-to-image applications such as Midjourney, Stable Diffusion, and DALL-E 2. The research analyzes neural networks, latent space mechanisms, generative adversarial networks, autoregressive models, and diffusion probabilistic models to explain how AI systems transform textual prompts into visual representations. Through experimental prompt engineering and comparative image generation tests, the study investigates the relationship between AI-assisted creativity, visual storytelling, representation processes, and design workflows. The paper critically discusses the implications of generative AI for architecture and visual culture, including authorship, ethics, copyright, bias, realism, and the transformation of creative practices. The research ultimately proposes an informed and critical approach to AI-assisted representation, emphasizing the evolving role of designers as curators and strategic decision-makers within AI-driven creative environments.
Hypotheses of Images and Architectural Spaces in the Age of Artificial Intelligence
The paper explores the relationship between artificial intelligence, architectural representation, and digital techno-cultures through experimental workflows combining text-to-image generation, text-to-3D modeling, parametric design, and AI-assisted visualization. The research investigates how generative AI tools such as Midjourney, ChatGPT, PointE, Dreamfusion, and Grasshopper can support the creation of architectural forms, semantic image transitions, authorial hybridizations, and morphogenetic spatial configurations. The study proposes a conceptual framework organized around different AI media categories, including textual AI, image generation, video generation, post-production systems, and AI-assisted parametric modeling. Through hybrid workflows integrating AI-generated Python scripts, parametric modeling in Rhinoceros/Grasshopper, and generative visual experimentation, the paper reflects on the epistemological, creative, ethical, and technological implications of AI in architecture and representation. The research emphasizes AI as a creative and assistive medium capable of generating new spatial hypotheses and experimental design processes within architecture and digital representation.
Between Impossible and Probable. Architectural Recognition Through Qualitative Evaluation of Artificial Intelligence Response
The paper investigates evaluation methodologies for AI-based virtual reconstruction of historical architecture, focusing on the uncertainty inherent in generative predictions. The research critically compares traditional analog reconstruction workflows with AI-driven reconstruction processes based on GAN networks, proposing a conceptual framework called the “Uncertainty Tree” to describe the decision-making chain involved in reconstruction. The study develops a qualitative evaluation methodology intended to complement quantitative pixel-based assessments by introducing prediction thresholds ranging from impossible to highly probable reconstructions. Through experiments on datasets of Greek Doric temples and Mudejar Romanesque churches, the research argues that specialized datasets and expert qualitative evaluation significantly improve the plausibility and reliability of AI-generated architectural reconstructions. The paper ultimately proposes a new epistemological framework for evaluating AI predictions in heritage reconstruction, emphasizing human supervision, dataset specificity, and iterative qualitative assessment.