Hallucinating History: Multi-Modal Reconstruction of Lost Cultural Heritage via Generative Adversarial Networks

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Authors: Sai Teja Battula
Cited by: 0

Abstract

Cultural heritage sites that have been destroyed through armed conflict, natural disasters, and long-term neglect represent permanent losses to historical knowledge. Traditional digital preservation requires comprehensive pre-destruction documentation either through laser scanning or through systematic photography. Such documentation exists for relatively few heritage monuments globally. Fragmented archival materials, including tourist photographs, historical postcards, amateur sketches, and travel literature, frequently survive site destruction. Multi-modal generative frameworks enable three-dimensional reconstruction from heterogeneous archival sources. Text-to-image generators synthesise visual content for undocumented structural regions. Neural Radiance Fields integrate authentic and generated imagery into continuous volumetric representations. Textual descriptions extracted from historical accounts guide the interpretation of ambiguous photographic regions. Visual evidence grounds abstract linguistic references to specific spatial configurations. Architectural historians' degree reconstruction outputs against standards that encompass structural plausibility, stylistic coherence, proportional accuracy, and decorative authenticity. Expert assessment confirms substantial qualitative improvements in structural fidelity compared to photograph-only baselines. The developed framework operates with minimal specialised documentation requirements. Public domain photography and historical publications constitute sufficient input materials. Transparent communication regarding the generative nature of outputs maintains scholarly integrity. Digital repatriation becomes possible for communities separated from physical monuments through destruction or displacement.

Fields of Study & Subjects

3D Surveying and Cultural Heritage Image Processing and 3D Reconstruction Generative Adversarial Networks and Image Synthesis

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
31 January 2026