This paper introduces BldgWeaver, a novel adaptive generative model for creating 3D building digital cousin (BDC) models using pre-trained Transformer architecture. Unlike traditional approaches that require complete 3D reconstruction with extensive visual data, BldgWeaver approximates building geometries using artificial intelligence-generated content to address data deficiencies in urban digital twin development. The proposed method employs a token-based approach to convert triangle mesh coordinates into discrete tokens for auto-regressive prediction, incorporating parallel conditional controls and an optimized footprint-masked training strategy. Experiments conducted on the PLATEAU dataset demonstrate our model’s capability to generate Level of Detail 2 (LoD2) building models with diverse roof structures, achieving an average 49% improvement in geometric proximity compared to basic LoD1 representations. The proposed model effectively addresses challenges in wide-range urban mapping by reducing data dependencies while maintaining satisfactory architectural fidelity.