The contribution examines the case study of the Church of S. Pietro a Coppito in L’Aquila, which underwent significant stylistic restoration in the 1960s, to explore the potential applications and reliability of generative AI in cultural heritage communication. In particular, according to the virtual reconstruction of the ancient configuration, the study investigates the impact of integrating graphical inputs alongside textual prompts for image generation, assessing their effectiveness as a means of guiding AI and improving the quality of the results achieved. Based on graphic, photographic, and historiographical sources, sketches of the interior views of the church were produced. Subsequently, various gen-AI platforms were tested to process these images, evaluating the algorithms’ ability not only to generate coherent and effective visuals but, above all, to understand and interpret the initial graphic inputs. The results show both potential and critical limitations: while AI can produce photorealistic and stylistically coherent renderings, it often introduces arbitrary elements and struggles with strict historical accuracy, especially in architectural and decorative details. The paper highlights the need for deeper human-AI interaction and emphasizes the importance of informed graphical input and critical evaluation in the application of genAI to cultural heritage representation.
Artistry, Technology and Challenges: The Subtle Balance Between Fake Results and Real Integration in the Use of AI for Image Generation on Medieval Frescos Reconstructions
AI-based image-generative tools significantly enhance creativity and digital reconstruction by accelerating workflows for reconstructing digital heritage. These tools apply to various contexts, such as archaeological sites, transformed urban areas, damaged buildings, unbuilt architecture, and partially lost artworks like mural paintings and frescoes. In reconstructing such works, the process is highly complex and requires scholars with expertise in recognizing and attributing fragments and better-preserved pieces. While it remains necessary to suggest potential lines, figures, or scenes, the outcome often involves multiple possibilities. AI image generators can support these challenging tasks by integrating them into workflows, from producing stochastic results to extending patterns and coloured areas. However, their use, especially via “off-the-shelf” software, introduces two key challenges. The first concerns achieving accurate reconstructions that maximize the AI’s potential while avoiding casual or tentative outputs. This involves addressing limitations in existing AI systems and ensuring the final results are both reliable and time efficient. The second challenge relates to cultural evaluation. The line between AI-assisted reconstruction and creating entirely fake results is delicate. Misuse could exploit public fascination with AI or lead to errors and misinterpretations without expert oversight. This integration is in its early stages, necessitating rigorous testing and exploration. The proposed contribution analyzes the digital reconstruction of medieval frescoes from central Italy, blending traditional methods with significant AI inputs. These case studies highlight the balance between human-driven processes and the acceptance of digital outputs, offering reflections on the evolving relationship between human expertise and AI-driven creativity.
Artificial Intelligence and Extended Reality for Communicating the Uses of Natural Fibers in Building Construction: State of the Art and New Proposals
This contribution presents a digital framework to promote and communicate the use of natural fibers in building construction, developed within the “Circular Design for Natural Fibers” (CD4NF) project. The research addresses the limited application of advanced digital tools in this emerging field by proposing an integrated workflow that synergizes Building Information Modeling (BIM), Artificial Intelligence (AI), and eXtended Reality (XR). The core of the proposal is a flexible and modular platform built upon a structured material filing system that catalogs detailed information on various natural fibers. This platform utilizes BIM as a central data repository. An AI system, based on a Retrieval-Augmented Generation (RAG) model, enables users to query this complex data using natural language. XR applications provide an interface for visualizing and interacting with the BIM models and their associated information in real-world contexts. This approach aims to optimize key processes from material research and design to construction, enhancing decision-making, interoperability, and communication. The system represents a step towards a more efficient, circular, and sustainable construction sector by facilitating the informed adoption of natural fiber-based materials.
TIBER: Test, Interact and Build in Extended Realities
The aim of the research project “Modeling the Morphology and Dynamics of Roman Mediterranean Port Cities,” funded by the LABEX Intelligence des Mondes Urbains of the University of Lyon, is to reconstruct lost urban spaces of antiquity while facilitating possible interactions between two scientific communities often brought together in our field: archaeologists and architect-computer scientists. It focuses primarily on a collaborative and interactive environment allowing these communities to interact by jointly and incrementally formalizing the scientific hypotheses that will serve to reconstruct the studied urban and port spaces in three dimensions. The concerted formalization of an urban space at a given period requires numerous interactions: verbal exchanges, visual demonstrations, iconographic comparisons, and morphological hypotheses. It is nourished by expansive preliminary documentary work constituting a state of knowledge at a given moment. However, we believe that when it comes to collaborating between heterogeneous communities, whose words, descriptions, understanding, and interpretation of space draw from different visual and scientific cultures, 3D visualization alone—as detailed and eloquent as it may be—is unable to provide the same interpretative keys to different disciplines. Debates pertaining to synthetic images, for instance, may deprive those who fail to master digital tools of a nuanced space of expression, which can ultimately unbalance the interpretations and shaping of the studied spaces. We have therefore decided to reconsider the nature of this interaction space by reinventing the REACTABLE© (Reactable Project Homepage, https://reactable.com/, last accessed 2025/05/13.), which is essentially a backlit translucent horizontal surface on which tangible objects are placed in order to manifest the appearance of their digital 3D double on a large screen, for example. Echoing what D. Schön calls the “reflective conversation with the materials of the situation” characteristic of the mental disposition of individuals in situations of exchange and debate, we endeavor to enrich possible levels of interaction between transdisciplinary communities by using concept-objects belonging to the real world that allow each individual around the table to participate in exchanges bolstered by the same capacity for interaction.
The Language of Photography in the Age of AI
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.
Exploring Evolutionary Optimization: Integration of AI and Additive Manufacturing
Contemporary architecture, as well as design, has revolutionised the approach to form creation, prioritising increasingly efficient and, above all, adaptive modelling. It is essential for designers to identify the requirements that the product to be realised must meet for its own production. This methodology, driven by technological advancements in the field of artificial intelligence (AI) and the use of advanced algorithms, enables the exploration and generation of optimised products from various perspectives based on chosen criteria; the resulting forms integrate functionality and aesthetics. This research proposes the use of tools within the parametric modelling environment Grasshopper, such as Galapagos and Octopus, which employ AI algorithms to enhance the design process and optimise forms for additive manufacturing. Experimentation with these plugins allows leveraging evolutionary algorithms to explore a wide range of design solutions, enabling designers to efficiently optimise complex forms. In this context, AI facilitates tackling multi-objective optimisation problems, improving parameters such as structural strength, material usage, and minimisation of printing times. This approach not only enhances the efficiency of the design process but also opens up new possibilities for innovation in design by integrating the advanced computational capabilities of AI with the creative potential of parametric design.
Artificial Creativity. Design Evolution in the Age of AI
The advent of artificial intelligence (AI), in combination with the ubiquity of digital sensors, computer networks, and automation that has characterised the last decade, is transforming the socio-economic environment and defining a probable new industrial era. This evolution also inevitably involves the world of Design by redefining the designer’s role, the object of design, and user relationships. Many of today’s artefacts, whether an iPhone application, a car, or a building, are increasingly connected to the designer who conceived them, thanks to a continuous flow of data detailing many aspects of the user experience. This same information can be used to train AI neural networks capable of autonomously generating specific solutions without human intermediation. An artificial intelligence engine can thus anticipate users’ needs and behaviours, proposing solutions that are improved and customised according to the particular use that distinguishes each customer. This paradigm shift has important implications for the role of the designer. Through the analysis of pioneering case studies, this paper analyses the possible developments, delving into the repercussions for design theory and practice concerning the theoretical framework used today to interpret the discipline of Design.
Advanced Digitization of Cultural Heritage via NeRF
Digitization processes, increasingly employed in various ways for the accessibility and preservation of cultural heritage, continuously engage with the diverse range of materials that define it. Objects like reflective ceramics and transparent glass have complex optical properties, which pose a unique challenge for digital acquisition methods such as photogrammetry and laser scanning, as well as for the modelling process. These materials often generate specular reflections or refract light, which interfere with the conventional algorithms used in the acquisition processes, leading to incomplete or inaccurate 3D models. This study explores the potential of Neural Radiance Fields (NeRF), an innovative 3D reconstruction technique based on deep learning, to overcome these limitations. By using volumetric encoding of scenes and simulating complex light interactions, NeRF captures phenomena like reflections and refractions with consistent realism. The exploration is carried out through a stress test on ceramic and glass materials, using both NeRF technology and traditional systems like digital photogrammetry. The comparison of the results highlights the advantages and disadvantages of both technologies, while emphasizing the current need for their complementarity, with workflows still largely hybrid.
AI-Enhanced UAV Photogrammetry Point Clouds for Cultural Heritage Assessment
Advancements in low-altitude remote sensing and image analysis have revolutionized the digitisation of real-world objects, initially represented as point clouds. Over the past decade, drone-based surveying has gained traction; however, noise during data capture and 3D reconstruction remains a critical challenge, affecting the accuracy and usability of UAV images in real-world applications. This study presents a novel approach to enhance cultural heritage assessment using deep learning models based on the Autoencoder Gaussian Mixture Clustering Model and the Autoencoder K-means Clustering Model. These models improve the accuracy of point clouds generated from UAV images for better condition assessment and survey. The research focuses on photogrammetric accuracy parameters for clustering point clouds. The new approach chooses K-means for finding global patterns due to its robust accuracy, and the Gaussian Mixture Clustering Model for local changes and inspection applications. Moreover, these models investigate the accuracy of point cloud clusters generated from K-means and the Gaussian Mixture Clustering Model. To validate the new approach, the study evaluates variations in key parameters under diverse built-environment conditions using the Temple of Neptune point cloud in Paestum, Italy. The results demonstrate significant improvements in point cloud 3D reconstruction, leading to more accurate surveys and assessments. The proposed method surpasses traditional techniques, offering enhanced applicability for point-cloud filtering. Furthermore, comparisons of clustering results highlight the algorithm, establishing its promising potential for advancing UAV-based surveying and inspection practices.
Semi-Automated Feature Recognition and Localization in 3D Point Cloud by Using AI
The application of Artificial Intelligence (AI) in Cultural Heritage surveying has gained notable interest, particularly in enhancing the classification and segmentation of 3D point clouds. Recent research focuses on automating processes to manage surveying data and integrating it into the BIM environment. The present research proposes a semi-automated AI-based pipeline to semantically classify architectural features in 3D point clouds and match them with an existing BIM library. The case study examines the school and theatre of the Crespi d’Adda industrial village, a UNESCO Cultural Heritage site. Data includes a 3D point cloud generated via terrestrial photogrammetry, with windows selected as the feature to model in the BIM library. The methodology encompasses three phases: (i) creating a Crespi d’Adda window dataset aligning with BIM library parameters; (ii) applying Machine Learning classifiers for semantic categorization; and (iii) using prediction algorithms to recognize windows in the point cloud, matching them to the BIM library, and calculating accuracy. This research bridges architectural representation and data mining, streamlining BIM reconstruction. Anticipated results include automated detection and labeling of elements with accurate placement in the BIM environment, enhancing efficiency and interdisciplinary integration.