Application of generative AI to realistic image rendering in virtual reconstruction: the crypt of the Royal Monastery of Saint Engracia in Zaragoza

Abstract

This study proposes a methodology that combines traditional two-dimensional (2D) drawing and three-dimensional (3D) modelling methods with generative artificial intelligence (GenAI) for the virtual reconstruction of destroyed buildings. This methodology can reduce production and texturing times without compromising the historical accuracy or photorealistic quality of the final images. As a case study, we propose to reconstruct the crypt of the Royal Monastery of Saint Engracia in Zaragoza, Spain, a space of Early Christian origin dating from the fourth century AD that reached its greatest extent in the 17th century. The complex was destroyed in the 19th century during the French invasion of 1808-1814 and subsequent interventions removed the surviving architectural remains. Consequently, no physical remains are available as a basis for the reconstruction. The methodology is therefore based on the analysis of historical documentation and architectural analogues. An initial floor-plan hypothesis was developed using AutoCAD 2025, followed by the creation of a detailed 3D model in Rhinoceros 8. In the final stage, the model was rendered without materials or textures in D5 Render. The resulting images were then processed using Nano Banana Pro, Gemini's image-generator tool, to generate textures and wall paintings with GenAI. Although the results were satisfactory, GenAI was found to introduce arbitrary artefacts or random interpretations into textures and architectural elements. For this reason, the tool should not operate autonomously, as human supervision is required to filter and validate each action and ensure that automatic generation does not compromise technical and historical accuracy. In conclusion, integrating GenAI into virtual reconstruction not only accelerates the workflow, but can also allow us to overcome the technical obstacles that hinder the virtual reconstruction of lost buildings, provided that its use is guided by scientific criteria and human oversight.