Historic buildings are important carriers of traditional Chinese culture; however, they currently face the dual challenges of long-term deterioration and inappropriate interventions. In Zhejiang Province, more than 75,000 immovable cultural heritage sites have been incorporated into the protection system, while a large number of historic buildings remain in urgent need of conservation. In response to the complexity of conventional digital reconstruction processes and the low efficiency of authenticity-oriented restoration, this study proposes an AI-generated content (AIGC)-driven Methodological Framework for efficient and authenticity-oriented digital reconstruction of historic buildings. Taking Xiangxian Hall in Changle Village, Lanxi, Zhejiang, as a case study, this research follows authenticity-oriented criteria, including accurate morphological characteristics, realistic materials and textures, realistic color and light–shadow effects representation, precise proportion and scale, and comprehensive historical and cultural representation. Based on these criteria, a systematic evaluation framework for authenticity-oriented digital reconstruction is established. Furthermore, an AIGC-driven full-process Methodological Framework is proposed that integrates intelligent knowledge retrieval, cross-modal generation models, AI-assisted image-based 3D reconstruction, and inverse rendering. The results demonstrate that the proposed approach, supported by AIGC technologies, can deeply integrate multi-source information, including field investigation data, the existing conditions of historic buildings, and historical and cultural context, thereby achieving efficient and authenticity-oriented digital reconstruction of historic buildings. Furthermore, it provides an integrated technical pathway and practical solution for the conservation and digital inheritance of historic buildings.
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Kexin et al. (2026) studied this question.
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