The study of archaeological signs and non-verbal markings requires navigating complex relationships between artefacts, symbolic forms, and contextual metadata. While digital heritage platforms increasingly provide cataloguing and metadata services, they still offer limited support for visual exploration and comparison, particularly when signs are small, spatially unconstrained, and embedded within deteriorated artefacts. This paper presents the International Etruscan Sigla Project (IESP), an integrated visual heritage system that combines structured cataloguing, intelligent document-based knowledge extraction, and AI-assisted visual similarity retrieval within a unified archaeological exploration environment. Rather than introducing these capabilities as independent tools, IESP supports the complete archaeological workflow, from knowledge acquisition and catalogue navigation to image-based exploration and comparative analysis. The system stores and models artefacts and sign elements, automatically extracts structured information from publicly available archaeological documents, and enables similarity-driven exploration through a dual-encoder architecture specifically designed for fine-grained comparison of archaeological signs. The proposed retrieval strategy aligns global sign representations with spatial feature maps extracted from artefact images, enabling similarity-based retrieval while preserving the local visual evidence required for archaeological interpretation. Users can interact with the system through multiple retrieval modalities, including sketch-based queries, image-based sign retrieval, and support-based exploration. The paper also presents the system's software architecture and reports the results of an expert-centred user study conducted through realistic archaeological tasks. The evaluation combines task performance, usability, cognitive workload, technology acceptance, and qualitative observations, demonstrating that the proposed interaction paradigm effectively supports archaeological exploration while preserving human interpretation and scholarly transparency. Overall, IESP demonstrates how integrating structured knowledge management, document understanding, and interpretable AI can transform digital heritage repositories into interactive environments that actively support archaeological research and visual analysis.
Multi-Light Image Collections (MLIC) are fundamental for virtual re-lighting and detailed surface analysis. In cultural heritage, the most common acquisition approach is free-form moving-light capture, where images taken under varying illumination are later processed to derive shape, material properties, or relightable image models. Despite its flexibility, this workflow remains limited by the need for trained operators, calibration targets, and often significant manual processing. To address these challenges, we present M \({{}^{2}}\) LIC (Mobile Multi-Light Image Collection), an automated, target-free pipeline utilizing video recordings from a fixed camera and a moving camera attached to an illuminator. Realizing this setup with just two commodity smartphones lowers hardware costs and technical barriers to entry by leveraging devices that users already own and operate daily. Our framework combines video synchronization, estimation of light directions directly from the scene structure, relighting-aware frame processing, and automatic generation of relightable models ready for web viewing. The protocol is fast and simple, requiring only the simultaneous recording of short videos, allowing operators to produce usable relightable models with minimal training and effort. Validated on a diverse set of artifacts, this open-source system helps lower the barrier to MLIC acquisition and aims to support more scalable MLIC digitization workflows.
Replicating the style of historical artistic traditions remains a notoriously challenging task for vision-based generative models, as artistic style encodes complex visual, cultural, and contextual dimensions that extend beyond surface-level appearance. Despite rapid advances in generative modeling, it remains unclear to what extent recent systems have achieved substantive improvements in stylistic emulation across diverse historical traditions. This article presents a systematic evaluation of contemporary generative models in artistic style imitation, comparing their outputs to those of earlier model generations. The study builds upon the AI-Pastiche dataset by incorporating six additional recent state-of-the-art models, thereby enabling a longitudinal assessment of performance progression over time. To ensure methodological consistency and comparability, the evaluation adopts the same experimental protocol used in the original release of the dataset. The analysis combines controlled prompt-based image generation with extensive user studies designed to assess multiple dimensions of aesthetic and stylistic performance. These dimensions include: (i) visual plausibility and perceptual realism; (ii) stylistic coherence and fidelity to historical conventions; (iii) versatility across a broad range of artistic traditions; (iv) adherence to prompt-specified content and stylistic constraints; and (v) the absence of structural artifacts or semantic inconsistencies. The results indicate substantial and measurable improvements in stylistic emulation in recent models. Nevertheless, persistent limitations remain, particularly in the consistent reproduction of deeper compositional structures and culturally embedded stylistic features. These findings suggest that progress in surface-level resemblance does not necessarily equate to structural or contextual understanding.
Intangible cultural heritage (ICH) embodies collective memory and creative practices, yet many traditions rooted in manual skills and oral transmission face increasing risk under globalization. Conventional digital documentation—such as images, videos, and static visualizations—provides archival value but offers limited support for computational style analysis and lacks real-time interactivity, particularly for engaging younger audiences. To address these limitations, this paper proposes ARIP, an integrative design framework for the AI-assisted interactive dissemination of pattern-based ICH. Using Yangzhou paper-cutting as a case study, ARIP organizes four complementary design functions—AI-mediated style modeling, real-time response, interactive participation, and pattern-oriented visual constraints—within a unified implementation process. A user study with secondary school students assessed self-reported engagement using the User Engagement Scale (UES). Participants in the interactive condition reported higher engagement across the measured UES dimensions than those in the conventional digital-material condition. Qualitative feedback further suggests that the interactive experience encouraged participants to attend to the visual characteristics and culturally associated motifs of paper-cutting. These findings provide preliminary evidence for the feasibility of ARIP in the Yangzhou paper-cutting case and suggest its potential as a design reference for future AI-assisted interactive heritage applications.
Archaeological sites and artifacts are critical for understanding historical contexts, and maintaining their integrity is a vital undertaking. At the same time, ensuring their accessibility to enable direct study and exploration is essential for both researchers and the general public. To this aim, various works have explored the use of Digital Twins (DTs) for Cultural Heritage (CH) preservation and dissemination. However, the current state of the art remains fragmented and heterogeneous, with most approaches relying on ad hoc, case-specific solutions that are rarely reusable. In particular, existing research is often centered on individual digitization projects rather than on scalable frameworks that support the systematic creation and reuse of multiple DTs. Building on these considerations, this paper presents a scalable platform designed to streamline the construction and exploitation of archaeological DTs, enabling the rapid creation of case studies for both research and dissemination purposes. Specifically, we propose a no-code authoring environment that integrates 3D reconstructions of sites and artifacts with associated semantic information (e.g., annotations and points of interest), allowing users with limited technical expertise to create and manage DT-based experiences. The resulting case studies can be edited, organized, shared, and explored through both desktop and Virtual Reality interfaces. The platform positioning with respect to existing solutions has been explicitly investigated through a comparative analysis against representative state-of-the-art systems. In addition, the proposed approach has been evaluated through quantitative performance measurements, a comparative study, and two user studies targeting the Research and Dissemination modes, respectively. The platform was co-designed with curators from the Egyptian Museum in Turin, Italy, and validated through case studies based on two archaeological sites from Deir el-Medina, Egypt.
