AHCI RESEARCH GROUP
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Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Huang, J.; Wang, C.; Li, L.; Huang, C.; Dai, Q.; Xu, W.
BuildingBlock: A Hybrid Approach for Structured Building Generation Proceedings Article
In: Spencer, S. N. (Ed.): Association for Computing Machinery, Inc, 2025, ISBN: 9798400715402 (ISBN).
Abstract | Links | BibTeX | Tags: 3D modeling, 3D models, 3d-modeling, Architecture, benchmarking, Building blockes, Construction, Data-driven model, Generative 3D Modeling, Hierarchical systems, Hybrid approach, Interactive computer graphics, Language Model, Layout generations, Procedural & Data-driven Modeling, Procedural content generations, Three dimensional computer graphics
@inproceedings{huang_buildingblock_2025,
title = {BuildingBlock: A Hybrid Approach for Structured Building Generation},
author = {J. Huang and C. Wang and L. Li and C. Huang and Q. Dai and W. Xu},
editor = {S. N. Spencer},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105013956460&doi=10.1145%2F3721238.3730705&partnerID=40&md5=a0815a6742f5e1d072f0f559410ce28b},
doi = {10.1145/3721238.3730705},
isbn = {9798400715402 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {Association for Computing Machinery, Inc},
abstract = {Three-dimensional building generation is vital for applications in gaming, virtual reality, and digital twins, yet current methods face challenges in producing diverse, structured, and hierarchically coherent buildings. We propose BuildingBlock, a hybrid approach that integrates generative models, procedural content generation (PCG), and large language models (LLMs) to address these limitations. Specifically, our method introduces a two-phase pipeline: the Layout Generation Phase (LGP) and the Building Construction Phase (BCP). LGP reframes box-based layout generation as a point-cloud generation task, utilizing a newly constructed architectural dataset and a Transformer-based diffusion model to create globally consistent layouts. With LLMs, these layouts are extended into rule-based hierarchical designs, seamlessly incorporating component styles and spatial structures. The BCP leverages these layouts to guide PCG, enabling local-customizable, high-quality structured building generation. Experimental results demonstrate BuildingBlock ’s effectiveness in generating diverse and hierarchically structured buildings, achieving state-of-the-art results on multiple benchmarks, and paving the way for scalable and intuitive architectural workflows. © 2025 Elsevier B.V., All rights reserved.},
keywords = {3D modeling, 3D models, 3d-modeling, Architecture, benchmarking, Building blockes, Construction, Data-driven model, Generative 3D Modeling, Hierarchical systems, Hybrid approach, Interactive computer graphics, Language Model, Layout generations, Procedural & Data-driven Modeling, Procedural content generations, Three dimensional computer graphics},
pubstate = {published},
tppubtype = {inproceedings}
}
Three-dimensional building generation is vital for applications in gaming, virtual reality, and digital twins, yet current methods face challenges in producing diverse, structured, and hierarchically coherent buildings. We propose BuildingBlock, a hybrid approach that integrates generative models, procedural content generation (PCG), and large language models (LLMs) to address these limitations. Specifically, our method introduces a two-phase pipeline: the Layout Generation Phase (LGP) and the Building Construction Phase (BCP). LGP reframes box-based layout generation as a point-cloud generation task, utilizing a newly constructed architectural dataset and a Transformer-based diffusion model to create globally consistent layouts. With LLMs, these layouts are extended into rule-based hierarchical designs, seamlessly incorporating component styles and spatial structures. The BCP leverages these layouts to guide PCG, enabling local-customizable, high-quality structured building generation. Experimental results demonstrate BuildingBlock ’s effectiveness in generating diverse and hierarchically structured buildings, achieving state-of-the-art results on multiple benchmarks, and paving the way for scalable and intuitive architectural workflows. © 2025 Elsevier B.V., All rights reserved.