# Towards interdisciplinary approaches combining AI and 2D/3D: Designing a digital environment for the virtual reconstruction of a lost medieval church using a historical ontology

Florent Laroche, Ambre Vilain, Mathis Vauxion, Elouarn Le Chenadec

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## ► To cite this version:

Florent Laroche, Ambre Vilain, Mathis Vauxion, Elouarn Le Chenadec. Towards interdisciplinary approaches combining AI and 2D/3D: Designing a digital environment for the virtual reconstruction of a lost medieval church using a historical ontology. Digital Heritage, université de sienne, Sep 2025, Sienne, Italy, Italy. 10.2312/dh.20253170. hal-05165876

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# Towards interdisciplinary approaches combining AI and 2D/3D: Designing a digital environment for the virtual reconstruction of a lost medieval church using a historical ontology

Florent Laroche¹, Ambre Vilain², Mathis Vauxion², Elouarn Le Chenadec¹

¹ Nantes University, Ecole Centrale, LS2N laboratory, France, florent.laroche@ls2n.fr

² Nantes University, LARA laboratory, CReAAH, France

## Abstract

This article explores the intersection of artificial intelligence (AI), 3D modeling, and historical ontology in the context of digital heritage. It presents an interdisciplinary methodology designed to enhance the accuracy and epistemological transparency of virtual reconstructions, using the lost Cordeliers Church of Nantes as a case study. Generative AI models, supported by curated databases and structured ontologies (OMeKA-S), are used to produce historically grounded visual hypotheses. The process includes source verification, bias analysis, and an iterative evaluation protocol by domain experts. The study also examines the ethical and methodological implications of using AI in the humanities, advocating for transparent, explainable, and human-centered AI workflows. By combining technical innovation with heritage expertise, this work offers a reproducible framework for digital museography and future research in the digital humanities.

## CCS Concepts

* Information systems → Information retrieval → Users and interactive retrieval

* Human-centered computing

* Computing methodologies → Artificial intelligence

## 1.Introduction

Digital museography has rapidly evolved in recent years. Research done alongside humanities and social sciences laboratories, has advanced expertise in virtual heritage, covering 3D digitization, knowledge management, computer-aided design, virtual reality, museography, and archiving. www.epotec.fr

This paper explains our project that aims to develop AI-driven tools and methods for precise historical image reconstruction, employing digital ontologies to guide AI reasoning. This aligns AI practices with traditional art historical and archaeological methodologies, including source recognition, bias analysis, data extraction, and knowledge application.

The case study focuses on the reconstruction of the lost interior and decorative elements of the Cordeliers Church in Nantes (France), using structured ontologies and generative AI algorithms. Our project also serves as a response to the opaque mass appropriation of web images and datasets by major AI providers. In contrast, our approach emphasizes ethical transparency, source traceability, and open-source code.

## 2. Context and problematic of AI for heritage

### 2.1 AI, cultural sovereignty and challenges for heritage

With the rapid democratization and popularization of digital tools [DM14]—such as XR headsets and audiovisual virtual assistants (chatbots)—now integrated into the everyday lives of citizens, their use raises crucial questions regarding the reliability of the results they generate and the ethical frameworks that surround them [Arc24]. More than ever, individuals can craft their own visual and editorial narratives from a handful of historical sources found online. This dynamic creates a growing tension between cultural sovereignty and the legitimacy of democratic systems already under pressure from global forces [LDD*23]. A notable illustration of this unease occurred in March 2023, when major tech firms and hundreds of experts issued a petition (https://futureoflife.org) warning against the escalating capabilities of AI and its potentially disruptive economic and political consequences.

Cultural heritage is by no means exempt from this paradigm shift. On the contrary, it has become a key terrain where the
promises and pitfalls of AI-driven technologies manifest most visibly. In this context, rethinking the role of researchers and heritage professionals becomes essential. As highlighted by Sauret [Sau22], integrating AI into the humanities is not only a technical issue but also a conceptual one, requiring careful consideration of the specific epistemologies, interpretative methods, and narrative practices inherent to Human Sciences disciplines. The use of AI in historical image reconstruction demands rigorous methods and critical tools to ensure both accuracy and scientific integrity. While AI models such as MidJourney or Leonardo offer fascinating creative potential, they often produce biased or anachronistic representations [BA25], due in part to the absence of structured ontologies capable of reflecting the multiplicity of humanistic perspectives [VV24]. When confronted with visual gaps or ambiguous documentation, these systems tend to default to familiar stylistic conventions—such as Neo-Gothic aesthetics—that may distort the cultural meaning of the historical subjects they attempt to represent.

They are few documented case studies or standardized best practices. The current state of the art highlights a proliferation of initiatives integrating these technologies into the humanities. Few structured methodological studies have been conducted to analyze their epistemological and operational impacts. The main issue lies in how these digital tools influence the production and interpretation of knowledge.

## 2.2 A case study: from chapel engraving to AI-g imagery

A telling example is the engraved representation of the Cordeliers Chapel in Nantes (figure 1), which was used as a basis for prompting a GPT-4o image generation. The objective was to recreate a historically plausible view in the style of a 19th-century postcard [VV24]. While the result successfully reproduced some iconographic tropes, it introduced several inaccuracies, notably in the depiction of stained-glass windows (figure 2). Although the image was visually appealing, it lacked architectural precision and misunderstood key symbolic elements. This example underscores the necessity of embedding generative processes within a critical methodology informed by historical expertise and source-based validation.

<figure><img src="image_3.png" /><figcaption>**Figure 1:** *an engraved representation of the Cordeliers Chapel*
*(M.Hawke, In: Guépin A., 1839 Histoire de Nantes)*</figcaption></figure>

These challenges are further amplified by broader issues of ethics and transparency. Many widely used AI tools are trained on untraceable data scraped from the web, resulting in opaque image-generation pipelines. Our approach contrasts sharply with this trend, prioritizing ethical transparency, provenance tracking, and open-source dissemination. The reconstruction of the Cordeliers Church is not only a technical undertaking, but also a response to the unchecked appropriation of cultural materials by commercial AI providers.

<figure><img src="image_4.png" /><figcaption>**Figure 2:** *historical reconstruction of the Cordeliers Chapel*
*(1839) with GPT-4o model*</figcaption></figure>

## 2.3 A pioneering methodology bridging AI and humanities

The main scientific question addressed in this work is: “How can AI and XR provide innovative solutions for research in the humanities and social sciences, particularly in the study of the past, heritage, and cultures?” Our research project does not aim to solve all technological bottlenecks. Rather, it is explicitly positioned to identify and help overcome scientific challenges related to how we construct knowledge of our culture and past. [ASS22] [GMW15] [FC19].

Obviously, it is ambitious to explorer both AI technologies and XR technologies. This paper will then concentrate on the first step of historical hypothesis creation thanks to AI.

Our research project, currently underway, is pioneering in its ambition to integrate generative AI into a heritage research framework without compromising scholarly rigor. We aim to
develop tools and methods for accurate historical image
reconstruction by combining generative models with structured
digital ontologies. Rather than using AI as a shortcut or
replacement for expertise, we treat it as a partner—embedded
within disciplinary practices such as source analysis, bias
detection, and interpretative modeling.

The methodology we propose is deliberately hybrid: it draws
on both the expressive capabilities of generative AI and the
precision of knowledge engineering. Ontologies are not simply
metadata structures, but active frameworks that guide and
constrain the model's interpretive space. In doing so, we align
algorithmic outputs with the epistemological values of historical
and archaeological inquiry.

## 2.4 Constructive and interdisciplinary use of AI

Our goal is not to romanticize the potential of AI, nor to portray
it as an existential threat. Instead, we adopt a constructive stance
that recognizes both the affordances and limits of generative
technologies. AI is not neutral: it embodies assumptions, patterns,
and gaps inherited from its training data. But when critically
framed, it can serve as a catalyst for new forms of historical insight.

How does AI impact the production of knowledge in the
humanities and social sciences? What new research paradigms do
such technological shifts imply for these disciplines [Kit14]? By
mirror effect, how do computer and engineering sciences could
enrich and adapt their practices to humanities specificities? These
challenges call for a broader reflection on how AI transforms
knowledge production in the humanities. As underlined by
Beaudouin et al. [BLM*22], artificial intelligence invites a
reconfiguration of epistemic paradigms within human sciences,
questioning traditional notions of authorship, interpretation, and
proof.

The Cordeliers case study illustrates this duality. The AI-
generated images, while flawed, sparked productive debates
among historians, encouraged reinterpretation of archival
materials, and highlighted blind spots in our existing
reconstructions. In this sense, AI can contribute to historical
research—not by delivering ready-made truths, but by opening
new avenues for inquiry and reflexivity.

This vision requires a decisively interdisciplinary approach
[AZdS22] [Cug23]. The growing importance of AI and XR
technologies in research—already shown to hold great potential for
the humanities and social sciences—demands collaboration across
fields. This is why our team brings together historians,
archaeologists, and digital scientists to co-construct models that
are not only technically effective but also epistemologically sound.
By combining methodological rigor with ethical awareness, we
aim to contribute to a new generation of digital heritage practices
that are both innovative and grounded in scholarly values.

AI must not produce misleading or meaningless results;
digital technologies should assist decision-making while always
maintaining the central role of human expertise. It is essential to

ensure critical engagement with sources and to define robust
validation protocols for the outcomes of this “rediscovered
culture.”

## 3. Case study: virtual reconstruction of a lost medieval church

The case study focuses on reconstructing heraldic decorations
and architectural spaces of the lost Cordeliers Church in Nantes
(France), using structured ontologies and generative AI
algorithms.

Located just two hundred meters from the cathedral, the
Cordeliers convent and its church were a major center of religious
and political life in Nantes. This role was reflected both in the
prestigious tombs of the local elite that lined the nave of Saint-
Michel Church, and in the processions of guilds, as well as the
parliamentary sessions and elections held within the convent.
Under the pressure of urban planning projects and the turmoil of
the Revolution, most of the church and convent buildings were
destroyed, followed by a slow disintegration from 1875 to 1924,
ending in the complete demolition of the church and, more
recently, the demolition of the chapels converted into housing in
2010.

Our goal is to produce an immersive virtual reality
environment based on an accurate 3D reconstruction of the church.
Textual (Figure 4) and iconographic (Figure 5) sources will be
automatically processed through generative AI models and digital
ontologies to ensure historical accuracy. All developed codes are
in open access, fostering transparency and allowing community
validation.

Figure 3 is a 3D hypothesis restitution drawing done manually
by historian. It represents the Nantes district where the Cordeliers
Chapel was built. In the low middle center of the figure, traces of
archaeological remains can still be found.

<figure><img src="image_5.png" /><figcaption>Figure 3: 3D hypothesis restitution of the Cordeliers Chapel</figcaption></figure>

### 3.1 Historical database and ontology

First objective is to create of a rigorous database, developed
specifically for this project. It gathers the essential research, textual
and iconographic archives, as well as personal work of scientist of
the object. Historical researches are enriched by digitized sources like Dubuisson-Aubenay’s narrative and the Gaignières image collection (Collecta).

The following methodology which is specific to historical reconstruction has been followed:

* the various chronological layers of the building,

* the different sources (primary or secondary) and the analysis of their biases,

* the fundamental concepts of architecture and art history.

<figure><img src="image_6.png" /><figcaption>**Figure 4: historical archives example [Ber02]**</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 5: stained glass windows historical archives example of Francis II de Bretagne at the Cordeliers Chapel [Bou15]**</figcaption></figure>

All data manipulated are structured on OMEKA-S for direct AI utilization. Figure 6 is an overview of the heritage database structured following CIDOC-CRM recommendations. Database is in open access: https://epotec.univ-nantes.fr/s/cordeliers-nantes/page/chapelle-ducale

<figure><img src="image_8.png" /><figcaption>**Figure 6: Online database realized by historians**</figcaption></figure>
## 3.2 Digital AI process developed

In order to create an immersive virtual reality environment based on a 3D reconstruction of the Cordeliers Church, we base our work on historical sources, while integrating generative AI models and digital ontologies to ensure historical accuracy.

Main tasks of the Digital AI process are defined as follows:

1. Structuring and analyzing historical ontologies: investigate and integrate relevant ontologies into OMEKA-S to structure historical data drawn from archives, excavations or 3D drawings

2. Developing intelligent prompts: translate historical concepts into technical specifications. Automate the generation of context-aware prompts based on historical constraints and available data

3. Implementing an AI pipeline: configure and train generative models to produce coherent and historically faithful images, grounded in validated sources. Figure 7 illustrates articulations between modules, database and results. It is a conceptual diagram demonstrating how our researches integrate AI into history and heritage

4. Validating results: compare generated images with historical descriptions and refine models produced. Design an interactive demonstration (museum prototype) for public presentation, using appropriate virtual reality technologies.

<figure><img src="image_9.png" /><figcaption>Figure 7: AI pipeline for generating historical hypothesis</figcaption></figure>

## 3.3 Training, AI-photogrammetry and scoring methodology

To create our historian artificial intelligence, it was therefore necessary to integrate primary and secondary sources, analyze their biases, extract data, curate and compare information. These inquiries are organized within a digital model (similar to Protégé 2000) and linked to databases, enabling full control over the process of information extraction and processing. Instead of learning isolated visual features, our AI models are trained to recognize contextual probability chains—e.g., the presence of a donor figure in stained glass increases the likelihood of an adjacent family coat of arms—thus respecting medieval iconographic conventions. The AI model was trained on a dedicated isolated machine within our research laboratory. A high-quality, annotated

image corpus was used and further refined through fine-tuning using the LoRA (Low-Rank Adaptation) technique [HSW*21]. Images are generated using Stable Diffusion XL [PEL*23] within the ComfyUI framework. The results are evaluated using a scoring system, which contributes to the training and improvement of the AI.

Generating a 3D virtual object directly was too optimistic. Then we first decided to create some 2D results in order to gather them as multiple pictures. Previous figure 7 illustrates:

* the global optimistic process (in blue)

* the real process followed (in orange)

We applied this methodology to LoRA-type fine-tuning (Low-Rank Adaptation), adapting a model (Stable Diffusion XL) from an annotated image corpus.

Our 'semantic funnel' approach prioritizes recurring motifs and historically documented associations. For example, the term 'chapel' automatically links to 'ribbed vault', as these elements systematically appear together. The AI thus learns not only isolated motifs but also contextual probability chains: the presence of a donor in stained glass raises the likelihood of a nearby family coat of arms, matching medieval conventions.

To structure generation of specific architectures, we integrated ControlNet [ZRA23], a module imposing geometric constraints on the generative model. Sketches of loadbearing walls, pillars or pointed-arch windows from archaeological plans serve as a basis. ControlNet segments the image (nave, side chapels) and guides stylistic filling while respecting these structures, avoiding spatial inconsistencies.

These tools help test reconstruction hypotheses — for example, how a 15th-century donor might appear near a stained-glass window in harmony with Gothic architecture. Their approximations provide a visual basis for discussion with art historians.

To ensure the quality of the generated visual hypotheses, we implemented a three-tiered evaluation protocol. First, internal uncertainty metrics assess the model's learning performance, complemented by semantic re-description via our in-house annotation tool. A domain-specific scoring system, detailed in section 3.4, evaluates spatial understanding, iconographic accuracy, stylistic and material coherence, and fidelity to sources.

Second, results are translated into a scale of historico-archaeological evidence based on Aparicio Resco and Figueiredo [ARF17], where color encodes the degree of reliability—from cold hues for speculative analogies to warm hues for well-documented reconstructions. Level 0 (black) indicates AI hallucinations. This visual coding is particularly effective in dense hypothesis zones, such as stained-glass iconography.

Finally, the BayesDiff overlay [KWD*24] adds a probabilistic dimension: while color reflects documentary support, transparency or hatching represents epistemic uncertainty. Together, these layers provide a clear, graduated indicator of confidence, guiding interpretation and mitigating the risk of overreading AI-generated content.
## 3.4 Analysis of results

Figures 8 and 9 compare interior architecture: figure 8 is a “human” hypothesis drawing and figure 9 was generated with ControlNet, based on a deliberately very general prompt aimed primarily at producing an architectural structure (e.g., “Gothic chapel”) by our own “historian IA”. Figure 10, is a stained-glass window generated with SDXL on the basis of a more targeted prompt - such as “Virgin and Child 15th century” - in order to obtain a specific iconographic motif.

Here is an analysis of the results shown in Figure 10, conducted by our historians based on five criteria:

<table><caption>**Table 1: Expert analysis of AI results**</caption><tr><td>Spatial understanding</td><td>Arrangement of figures is readable but somewhat chaotic; composition fits within a lancet and is framed reasonably well by micro-architectural elements.</td><td>7/10</td></tr><tr><td>Recognition of iconographic and architectural codes</td><td>One additional figure disturbs the scene, but without it the image could nearly represent a Virgin and Child; gestures are almost plausible.</td><td>7/10</td></tr><tr><td>Complex stylistic coherence</td><td>Grisaille very well interpreted; flamboyant influence understood in architectural details; almost no stylistic hybridization except for faces, which do not reflect 15th-century style; color distribution is chaotic but the tones are appropriate.</td><td>7/10</td></tr><tr><td>Faithfulness to textual and visual sources</td><td>Iconography generally understood; presence of a scroll slightly over-interpreted, but close to a typical stained-glass window.</td><td>7/10</td></tr><tr><td>Material and coherence</td><td>Very good coherence in the glass structure, assembly of pieces, palette, and lighting.</td><td>8/10</td></tr></table>

In this project, the statistical recurrence of certain motifs, such as twisted pillars in 15th-century Nantes chapels, informed the prioritization of design elements during generation. This shows how probability science, the foundation of generative AI, can meaningfully inform historical reasoning.

It is critical to emphasize that AI does not produce 'truth' but rather visual interpretations grounded in probabilistic logic. These visual hypotheses must be examined critically by historians to avoid misleading historical reconstructions.

## 3.4 Epistemological and Interdisciplinary Challenges

The project highlights critical epistemological questions for accurate heritage representation. A critical approach reevaluates methods of producing and validating historical knowledge, aiming to surpass traditional representational biases and enable rigorous digital continuity of archives. [MAT*16] [Kan22] [Ste23]

This interdisciplinary methodology merges historical and epistemological knowledge with technical skills in digital modeling and AI, promoting ethically sound, authentic digital reconstruction aligned with UNESCO standards [Une05].

<figure><img src="image_10.png" /><figcaption>**Figure 8: Hypothesis drawing by Eugénie Grimaud of the interior of the Cordeliers Chapel**</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 9: Reconstruction by the SDXL model (GWENN 0.4) combined with a LoRA and ControlNet of the ducal chapel of the Cordeliers of Nantes in the 15th century. Prompt: gothic flamboyant style 15th-century chapel, ribbed vault, pale limsetone, stained-glass windows, stone altar, intricate stone tracery, realistic lighting**</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Figure 10:** Reconstruction by the SDXL model (GWENN 0.4) combined with a LoRA of a 15th-century Flamboyant Gothic-style-stained glass window depicting a Virgin and Child</figcaption></figure>

**4. Synthesis, discussion and perspectives**

This scientific article contributes to ongoing interdisciplinary efforts to structure the use of AI and extended reality in the humanities and social sciences.

By proposing a rigorous methodology based on structured ontologies, curated historical datasets, and validation protocols, it demonstrates how generative AI can be responsibly integrated into heritage research. While current generative models often produce anachronistic or biased outputs, our results show that carefully designed workflows can significantly enhance the historical accuracy and interpretative value of virtual reconstructions. This approach reinforces the central role of human expertise, ensuring traceability, transparency, and epistemological integrity.

Our research is regarded as pioneering within our specialized historical field. The historians on our team are genuinely enthusiastic about the outcomes, as machine learning provides powerful tools to enhance their understanding of the past. This approach challenges traditional norms and practices in the heritage sector. Nonetheless, we remain attentive to comparable initiatives, aiming to benchmark our results, engage in constructive critique, and contribute to the broader evolution of the field. While predictive AI has been around for four decades, generative AI represents a rapidly developing scientific frontier.

Moreover, the study highlights the potential of immersive technologies such as XR not only for research but also for education, digital museography, and public engagement. Future

developments will aim to generalize this framework to other case studies and refine best practices for the ethical integration of AI tools into historical research.

**Acknowledgements**

This work is supported by the French ministry of culture and ELIT Cluster from NExT funded by French National research Agency.

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