Text-to-image algorithms based on Deep Learning are central to content creation in multiple application domains. In the last few years, the capacity of Neural Networks to generate increasingly realistic images quickly has blurred the boundary between authentic and realistic content, making genuine and false data less and less distinguishable. This condition leads to a profound reflection on the application of photographic images as a tool for communication and storytelling, trying to answer simple questions. Can today’s Neural Networks generate content comparable and indistinguishable from a photograph in both formal and compositional terms? Can artificial intelligence algorithms replace the photographer’s ability to design and obtain images that preserve the story and the place’s intangible culture? From a set of photographic rules framed in specific workflows, the research analyses some results obtained using text-to-image algorithms within the Midjourney program. The experiment aims to determine the pros and cons of using text-to-image algorithms to automatically generate photographic images, highlighting the potential and current limitations in constructing content subject to specific formal rules.
GPTfor Treatise Image Creation: ACritical Overview
Text-to-image systems based on generative pre-trained transformers have become pervasive in recent years. This result is mainly due to the generation tools’ ease of use, application availability, and increasingly refined graphical and algorithmic capabilities. On the other hand, the non-controllability given by a random process and the dependency on existing information databases highlight some limitations of these automatic image-generation methods. The rapid construction of increasingly realistic digital images draws new boundaries between real and unreal, highlighting a strict relation between text and image regarding semantics and logic descriptions. Therefore, a critical look into the use of these applications as tools for a reliable representation of architecture becomes cogent. We started from representations established by the treatises, as in the case of architectural orders. Most of these drawings, proportions, and rules are derived from descriptive parts in Vitruvius’ text. However, the graphic interpretations result from the architect’s experience and culture, which has become the basic grammar of architecture. This research stems precisely from the connection between Vitruvius’ text, the new text-to-image contents, and the established representations of the treatise writers. The comparison considers both image reliability and geometric rules, testing the current potential of GPT systems for image creation and reliability.
Index – Representation Across Boundaries New Links with AI, AI-GEN, and XR Tools for Cultural Heritage and Innovative Design
Preface – Representation Across Boundaries New Links with AI, AI-GEN, and XR Tools for Cultural Heritage and Innovative Design
Representation Across Boundaries: New Paradigms in the Age of AI and XR
The introduction and rapid expansion of new algorithms based on Machine Learning (ML) and Deep Learning (DL) processes to support knowledge and design activities has revolutionized multiple domains in recent years. Among these, research in the fields of Cultural Heritage, Design, and Architecture is fostering the development of new methodologies for study and content creation — partly supporting existing tools and partly replacing them entirely — while offering a new paradigmatic perspective on the impact of AI within these domains.
More specifically, the introduction of Generative AI (GenAI) and the creation of new forms of content within these fields open new possibilities for the understanding, analysis, design, and communication of architecture and design. At the same time, these developments highlight the limitations and risks associated with their uncritical use and raise important ethical questions. Human guidance and supervision in generative processes still remain — fortunately — a foundational component of these workflows, ensuring control over results while encouraging their implementation across different areas.
Through a concise review of current research in the field, the article provides an updated overview of recent international studies, while anticipating possible future developments concerning XR and AI in Cultural Heritage, Design, and Architecture.
Index – Advances in Representation New AI- and XR-Driven Transdisciplinarity
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.
Preface: Introducing the Relationships Between Digital Representation and AR/AI Advanced Experiences Preface – Digital Innovations in Architecture, Engineering and Construction
AR-Bicycle: Smart AR Component Recognition to Support Bicycle’s Second Life
This paper presents an augmented reality–based system for object and component recognition aimed at supporting product maintenance, repair, and lifecycle extension within a circular economy framework. The research focuses on bicycles as a case study, proposing a mobile application that combines 3D modeling, computer vision, and AR visualization to identify components and link them to contextualized repair information. The methodology integrates geometric analysis, average-shape modeling, and deep learning–based object recognition to enable scalable detection across different bicycle types. The workflow includes the definition of points of interest (PoIs), the creation of a generalized 3D model, and its implementation within Unity and Vuforia environments for AR interaction. As illustrated in the methodological diagram (Fig. 4, p. 613), the system connects recognition, information retrieval, and user interaction through a structured pipeline. The application allows users to visualize repair instructions, access multimedia content, and locate nearby service points, bridging digital knowledge and physical intervention. Results demonstrate the effectiveness of AR in improving component awareness, facilitating repair practices, and promoting sustainable product use, while highlighting limitations related to geometric variability and recognition accuracy across different product typologies.
Augmented Street Art: a Critical Contents and Application Overview
Street art is a growing phenomenon. The frequent appearance of works, projects, and events in this area reveals its increasing social and cultural role worldwide. The chance of digitizing art represents a benefit to defining cultural paths on the territory, providing an additional tool to understand and interpret it. Street art is characterized by peculiar aspects that make it unique in the artistic panorama. The democratization of contents and the physical decay of the work are two pillars. Any digitalization and communication project should consider them carefully, proposing a knowledge model respectful of the art. Augmented Reality (AR) is a representation tool that leads to achieving that delicate bal-ance between the real and the digital, enhancing the specificities of both. The authors start from the experimentation about artwork digitalization, connecting image deterioration with image recognition. Besides, they show some possible applications in Rome through a critical analysis of the domain, open-ing some future multidisciplinarity scenarios.