Generative AI models for different steps in architectural design: A literature review

Abstract

Recent advances in generative artificial intelligence (AI) technologies have been significantly driven by models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and denoising diffusion probabilistic models (DDPMs). Although architects recognize the potential of generative AI in design, personal barriers often restrict their access to the latest technological developments, thereby causing the application of generative AI in architectural design to lag behind. Therefore, it is essential to comprehend the principles and advancements of generative AI models and analyze their relevance in architecture applications. This paper first provides an overview of generative AI technologies, with a focus on probabilistic diffusion models (DDPMs), 3D generative models, and foundation models, highlighting their recent developments and main application scenarios. Then, the paper explains how the abovementioned models could be utilized in architecture. We subdivide the architectural design process into six steps and review related research projects in each step from 2020 to the present. Lastly, this paper discusses potential future directions for applying generative AI in the architectural design steps. This research can help architects quickly understand the development and latest progress of generative AI and contribute to the further development of intelligent architecture.

Keywords

Generative AI / Architectural design / Diffusion models / 3D generative models / Large-scale models

References

[1] Aalaei, M. , Saadi, M. , Rahbar, M. , Ekhlassi, A. , 2023. Architectural layout generation using a graph-constrained conditional generative adversarial network (gan). Autom. ConStruct. 155, 105053.

[2] Akdoǧan, M. , Balaban, Ö., 2022. Plan generation with generative adversarial networks: haeckel's drawings to palladian plans. Journal of Computational Design 3 (1), 135- 154.

[3] Alaçam, S. , Karadag, I. , Güzelci, O.Z. , 2022. Reciprocal style and information transfer between historical Istanbul Pervititch Maps and satellite views using machine learning. In: Estoa. Revista de la Facultad de Arquitectura y Urbanismo de la Universidad de Cuenca, vol. 11, p. 7, 22.

[4] Allen, E. , Iano, J. , 2019. Fundamentals of Building Construction: Materials and Methods. John Wiley & Sons.

[5] Ampanavos, S. , Malkawi, A. , 2021. Early-phase performance-driven design using generative models. In: International Conference on Computer-Aided Architectural Design Futures. Springer, pp. 87-106.

[6] Asmar, K. , Sareen, H. , 2020. Machinic interpolations: a gan pipeline for integrating lateral thinking in computational tools of architecture. In: Proceedings of the 24th Conference of the Iberoamerican Society of Digital Graphics. Online, pp. 18-20.

[7] Baudoux, G. , 2024. The benefits and challenges of artificial intelligence image generators for architectural ideation: study of an alternative human-machine co-creation exchange based on sketch recognition. In: International Journal of Architectural Computing, 2024, vol. 22. SAGE Publications Sage UK, London, England, pp. 201-215, 2.

[8] Brown, T. , Mann, B. , Ryder, N. , Subbiah, M. , Kaplan, J.D. , Dhariwal, P. , Neelakantan, A. , Shyam, P. , Sastry, G. , Askell, A. , et al., 2020. Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877- 1901.

[9] Cai, Z. , Lin, Y. , Li, J. , Zhang, Z. , Huang, X. , 2021. Building facade completion using semantic-synchronized gan. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 6387-6390. IEEE.

[10] Carey, S. , 2000. The origin of concepts. J. Cognit. Dev. 1 (1), 37-41. Taylor & Francis.

[11] Chaillou, S. , 2020. Archigan: artificial intelligence x architecture. In: Architectural Intelligence: Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2019). Springer, pp. 117-127.

[12] Chen, W. , Hays, J. , 2018. Sketchygan: towards diverse and realistic sketch to image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9416-9425.

[13] Chen, J. , Stouffs, R. , 2021. From exploration to interpretation: adopting deep representation learning models to latent space interpretation of architectural design alternatives. In: Proceedings of the 26th International Conference on ComputerAided Architectural Design Research in Asia (CAADRIA), pp. 131-140.

[14] Chen, J. , Shao, Z. , Hu, B. , 2023a. Generating interior design from text: a new diffusion model-based method for efficient creative design. Buildings 13 (7), 1861.

[15] Chen, J. , Wang, D. , Shao, Z. , Zhang, X. , Ruan, M. , Li, H. , Li, J. , 2023b. Using artificial intelligence to generate master-quality architectural designs from text descriptions. Buildings 13 (9), 2285.

[16] Chen, R. , Zhao, J. , Yao, X. , Jiang, S. , He, Y. , Bao, B. , Luo, X. , Xu, S. , Wang, C. , 2023c. Generative design of outdoor green spaces based on generative adversarial networks. Buildings 13 (4), 1083.

[17] Chen, Y. , Chen, Z. , Zhang, C. , Wang, F. , Yang, X. , Wang, Y. , Cai, Z. , Yang, L. , Liu, H. , Lin, G. , 2023d. Gaussianeditor: swift and controllable 3d editing with Gaussian splatting arXiv preprint arXiv: 2311.14521.

[18] Cheng, K. , Neisch, P. , Cui, T. , 2023. From concept to space: a new perspective on aigc-involved attribute translation. Digit. Creativ. 34 (3), 211- 229.

[19] Ching, F.D.K. , 2023a. Architecture: Form, Space, and Order. John Wiley & Sons.

[20] Ching, F.D.K. , 2023b. Architectural Graphics. John Wiley & Sons.

[21] Chuang, C.L. , Chien, S.F. , et al., 2021. Facilitating architect-client communication in the pre-design phase. In: Projections-Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia, CAADRIA 2021, vol. 2, pp. 71-80.

[22] Çiçek, S. , Turhan, G.D. , Tasşer, A. , 2023. Deterioration of pre-war and rehabilitation of post-war urbanscapes using generative adversarial networks. Int. J. Architect. Comput. 21 (4), 695- 711.

[23] de Miguel Rodríguez, J. , Villafañe, M.E. , Piškorec, L. , Caparrini, F.S. , 2020. Generation of geometric interpolations of building types with deep variational autoencoders. Design Science 6, e34.

[24] Del Campo, M. , Leach, N. , 2022. Can machines hallucinate architecture? AI as design method. Architect. Des 92 (3), 6- 13.

[25] Del Campo, M. , Manninger, S. , Sanche, M. , Wang, L. , 2019. The church of AI—an examination of architecture in a posthuman design ecology. In: Intelligent & Informed-Proceedings of the 24th CAADRIA Conference. Victoria University of Wellington, Wellington, New Zealand, pp. 15-18.

[26] Deng, Q. , Li, X. , Liu, Y. , Hu, K. , 2023. Exploration of threedimensional spatial learning approach based on machine learningetaking taihu stone as an example. Architectural Intelligence 2, 274- 284.

[27] Devlin, J. , Chang, M.-W. , Lee, K. , Toutanova, K. , 2018. Bert: pretraining of deep bidirectional transformers for language understanding.

[28] Dong, S. , Wang, W. , Li, W. , Zou, K. , 2021. Vectorization of floor plans based on edgegan. Information 12 (5), 206.

[29] Dong, J. , Jiang, Q. , Wang, A. , Wang, Y. , 2023. Urban cultural inheritance. In: HUMAN-CENTRIC, Proceedings of the 28th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2023, vol. 1, pp. 473-482.

[30] Doumpioti, C. , Huang, J. , 2022. Field condition-environmental sensibility of spatial configurations with the use of machine intelligence. In: Co-creating the Future: Inclusion in and through Design-Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022), vol. 2, pp. 67-74.

[31] Du, Z. , Shen, H. , Li, X. , Wang, M. , 2020. 3d building fabrication with geometry and texture coordination via hybrid gan. J. Ambient Intell. Hum. Comput. 13, 5177- 5188.

[32] Ennemoser, B. , Mayrhofer-Hufnagl, I. , 2023. Design across multiscale datasets by developing a novel approach to 3dgans. Int. J. Architect. Comput. 21, 358- 373.

[33] Farshad, A. , Yeganeh, Y. , Chi, Y. , Shen, C. , Ommer, B. , Navab, N. , 2023. Scenegenie: scene graph guided diffusion models for image synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 88-98.

[34] Fei, Y. , Liao, W. , Huang, Y. , Lu, X. , 2022a. Knowledge-enhanced generative adversarial networks for schematic design of framed tube structures. Autom. ConStruct. 144, 104619.

[35] Fei, Y. , Liao, W. , Zhang, S. , Yin, P. , Han, B. , Zhao, P. , Chen, X. , Lu, X. , 2022b. Integrated schematic design method for shear wall structures: a practical application of generative adversarial networks. Buildings 12 (9), 1295.

[36] Fei, Y. , Liao, W. , Lu, X. , Taciroglu, E. , Guan, H. , 2023. Semi-supervised learning method incorporating structural optimization for shear-wall structure design using small and long-tailed datasets. J. Build. Eng. 79, 107873.

[37] Fu, B. , Gao, Y. , Wang, W. , 2023. Dual generative adversarial networks for automated component layout design of steel framebrace structures. Autom. ConStruct. 146, 104661.

[38] Gao, X. , Guo, X. , Lo, T. , 2023. M-strugan: an automatic 2d-plan generation system under mixed structural constraints for homestays. Sustainability 15 (9), 7126.

[39] Ghannad, P. , Lee, Y.-C. , 2022. Automated modular housing design using a module configuration algorithm and a coupled generative adversarial network (cogan). Autom. ConStruct. 139, 104234.

[40] Goodfellow, I. , Pouget-Abadie, J. , Mirza, M. , Xu, B. , WardeFarley, D. , Ozair, S. , Courville, A. , Bengio, Y. , 2014. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 35, 53- 65.

[41] Gregor, K. , Danihelka, I. , Graves, A. , Rezende, D. , Wierstra, D. , 2015. Draw: a recurrent neural network for image generation. In: International Conference on Machine Learning, vol. 37. PMLR, pp. 1462-1471.

[42] Gu, Y. , Huang, Y. , Liao, W. , Lu, X. , 2024. Intelligent design of shear wall layout based on diffusion models. Comput. Aided Civ. Infrastruct. Eng. 16 (2), 126- 142.

[43] Gui, Y. , Zhou, B. , Xie, X. , Li, W. , Zhou, X. , 2021. GAN-based method for generative design of visual comfort in underground space. In: IOP Conference Series: Earth and Environmental Science, vol. 861. IOP Publishing. 072015.

[44] Güzelci, O.Z. , 2022. A machine learning-based model to predict the cap geometry of Anatolian Seljuk Kümbets. Periodica Polytechnica Architecture 53 (3), 207- 219.

[45] Han, S. , Jiang, Y. , Huang, Y. , Wang, M. , Bai, Y. , Spool-White, A. , 2023. Scan2Drawing: use of deep learning for as-built model landscape architecture. J. Construct. Eng. Manag. 149 (5).

[46] Han, Z. , Yuan, Y. , Stouffs, R. , Li, X. , 2024. GRAPH2PIX: a generative model for converting room adjacency relationships into layout images. In: Proceedings of the 29th International Conference of the Association for ComputerAided Architectural Design Research in Asia (CAADRIA) 2024, vol. 1, pp. 139-148.

[47] Hanafy, N.O. , 2023. Artificial intelligence's effects on design process creativity: a study on used AI text-to-image in architecture. J. Build. Eng. 80, 107999.

[48] Hasey, M. , Rhee, J. , Cardoso Llach, D. , 2023. Form data as a resource in architectural analysis: an architectural distant reading of wooden churches from the Carpathian mountain regions of Eastern Europe. Digit. Creativ. 34 (2), 103- 126.

[49] He, W. , 2022. Sequential masterplanning: using urban-GANs. Architect. Des 92 (3), 100-107. Wiley Online Library.

[50] He, Q. , Li, Z. , Gao, W. , Chen, H. , Wu, X. , Cheng, X. , Lin, B. , 2021a. Predictive models for daylight performance of general floorplans based on CNN and GAN: a proof-of-concept study. Build. Environ. 206, 108346.

[51] He, S. , Liao, W. , Yang, M.Y. , Yang, Y. , Song, Y.-Z. , Rosenhahn, B. , Xiang, T. , 2021b. Context-aware layout to image generation with enhanced object appearance. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15049-15058.

[52] Herzog, T. , Krippner, R. , Lang, W. , 2004. Facade construction manual. DETAIL-Institut für internationale Architektur-Dokumentation GmbH & Co. KG.

[53] Ho, J. , Jain, A. , Abbeel, P. , 2020. Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840- 6851.

[54] Ho, J. , Salimans, T. , Gritsenko, A. , Chan, W. , Norouzi, M. , Fleet, D.J. , 2022. Video diffusion models. In: Advances in Neural Information Processing Systems, 35, pp. 7070-7082 arXiv: 2204.03458.

[55] Horvath, A.-S. , Pouliou, P. , 2024. AI for conceptual architecture: reflections on designing with text-to-text, text-to-image, and image-to-image generators. Frontiers of Architectural Research 13 (3), 593-612. Elsevier.

[56] Hosseini, S. , Furukawa, Y. , 2022. Floorplan restoration by structure hallucinating transformer cascades arXiv preprint arXiv: 2206.00645.

[57] Huang, J. , Johanes, M. , Kim, F.C. , Doumpioti, C. , Holz, G.-C. , 2021. On gans, nlp and architecture: combining human and machine intelligences for the generation and evaluation of meaningful designs. Technologyd Architecture+ Design 5 (2), 207- 224.

[58] Huang, C. , Zhang, G. , Yao, J. , Wang, X. , Calautit, J.K. , Zhao, C. , An, N. , Peng, X. , 2022. Accelerated environmental performance-driven urban design with generative adversarial network. Build. Environ. 224, 109575.

[59] Huang, S.-Y. , Llabres-Valls, E. , Tabony, A. , Castillo, L.C. , 2023a. Damascus house: exploring the connectionist embodiment of the islamic environmental intelligence by design. In: eCAADe Proceedings, vol. 1, pp. 871-880 eCAADe.

[60] Huang, Z. , Xie, H. , Fukusato, T. , Miyata, K. , 2023b. Anifacedrawing: anime portrait exploration during your sketching. In: ACM SIGGRAPH 2023 Conference Proceedings, vol. 14, pp. 1-11.

[61] Isola, P. , Zhu, J.-Y. , Zhou, T. , Efros, A.A. , 2017. Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125-1134.

[62] Jiang, F. , Ma, J. , Webster, C.J. , Li, X. , Gan, V.J. , 2023. Building layout generation using site-embedded GAN model. Autom. ConStruct. 151, 104888.

[63] Jo, H. , Lee, J.-K. , Lee, Y.-C. , Choo, S. , 2024. Generative artificial intelligence and building design: early photorealistic render visualization of façades using local identity-trained models. Journal of Computational Design and Engineering 11 (2), 85- 105.

[64] Johanes, M. , Huang, J. , 2023. Generative isovist transformer: machine learning for spatial sequence synthesis. In: 41st Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2023, pp. 471-480.

[65] Karadag, I. , Güzelci, O.Z. , Alaçam, S. , 2023. Edu-AI: a twofold machine learning model to support classroom layout generation. Construct. Innovat. 23 (4), 898- 914.

[66] Karras, T. , Laine, S. , Aila, T. , 2019. A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401-4410.

[67] Kerbl, B. , Kopanas, G. , Leimkühler, T. , Drettakis, G. , 2023. 3D Gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. 42 (4).

[68] Kim, D. , 2022. Latent morphologies: encoding architectural features and decoding their structure through artificial intelligence. Int. J. Architect. Comput. 2023, 1- 20.

[69] Kim, F.C. , Huang, J. , 2022. Towards a machine understanding of architectural form. In: Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2022, vol. 1, pp. 727-736.

[70] Kim, F.C. , Huang, J. , 2023. Perspectival GAN. In: Co-creating the Future, Education and Research in Computer Aided Architectural Design in Europe (eCAADe), vol. 1, pp. 341-350.

[71] Kim, S. , Kim, D. , Choi, S. , 2020. CityCraft: 3D virtual city creation from a single image. Vis. Comput. 36, 911- 924.

[72] Kim, D. , Guida, G. , García, J.L. , López, D.C.Y. , 2022. Participatory urban design with generative adversarial networks. In: Proceedings of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia(CAADRIA), vol. 2, pp. 486-494.

[73] Kim, D. , Lee, L.S. , Kim, H. , 2023. Elemental sabotage. In: HUMANCENTRIC, Proceedings of the 28th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2023, vol. 1, pp. 29-38.

[74] Kim, F.C. , Johanes, M. , Huang, J. , 2023. Text2form diffusion: framework for learning curated architectural vocabulary. In: 41st Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2023, pp. 79-88.

[75] Kirillov, A. , Mintun, E. , Ravi, N. , Mao, H. , Rolland, C. , Gustafson, L. , Xiao, T. , Whitehead, S. , Berg, A.C. , Lo, W.-Y. , et al., 2023. Segment anything arXiv preprint arXiv: 2304.02643.

[76] Koehler, D. , 2023. More than anything: advocating for synthetic architectures within large-scale language-image models. Int. J. Architect. Comput. 21, 242- 255.

[77] Koh, I. , 2023. AI-bewitched architecture of hansel and gretel: foodto-architecture in 2D & 3D with GANs and diffusion models. In: Proceedings of the CAADRIA Conference, pp. 9-18.

[78] Kösenciǧ, K.Ö. , Okuyucu, E.B. , Balaban, Ö. , 2024. Structural plan schema generation through generative adversarial networks. Nexus Netw. J. 26, 409- 427.

[79] Li, J. , Guo, F. , Chen, H. , 2023a. A study on urban block design strategies for improving pedestrian-level wind conditions: CFD-based optimization and generative adversarial networks. Energy Build. 304, 113863.

[80] Li, Y. , Liu, H. , Wu, Q. , Mu, F. , Yang, J. , Gao, J. , Li, C. , Lee, Y.J. , 2023b. Gligen: open-set grounded text-to-image generation arXiv preprint arXiv: 2301.07093.

[81] Li, J. , Luo, Y. , Lu, S. , Zhang, J. , Wang, J. , Guo, R. , Wang, S. , 2024. CHATDESIGN: bootstrapping generative floor plan design with pre-trained large language models. In: Proceedings of the 29th International Conference of the Association for ComputerAided Architectural Design Research in Asia (CAADRIA) 2024, vol. 1, pp. 99-108.

[82] Liao, W. , Lu, X. , Huang, Y. , Zheng, Z. , Lin, Y. , 2021. Automated structural design of shear wall residential buildings using generative adversarial networks. Autom. ConStruct. 132, 103931.

[83] Liao, W. , Huang, Y. , Zheng, Z. , Lu, X. , 2022. Intelligent generative structural design method for shear wall building based on "fused-text-image-to-image" generative adversarial networks. Expert Syst. Appl. 210, 118530.

[84] Liao, W. , Wang, X. , Fei, Y. , Huang, Y. , Xie, L. , Lu, X. , 2023. Baseisolation design of shear wall structures using physics-rule-coguided self-supervised generative adversarial networks. Earthq. Eng. Struct. Dynam. 52, 3281- 3303.

[85] Lin, C.-H. , Gao, J. , Tang, L. , Takikawa, T. , Zeng, X. , Huang, X. , Kreis, K. , Fidler, S. , Liu, M.-Y. , Lin, T.-Y. , 2023a. Magic3d: highresolution text-to-3D content creation. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 300-309.

[86] Lin, H. , Huang, L. , Chen, Y. , Zheng, L. , Huang, M. , Chen, Y. , 2023b. Research on the application of cGAN in the design of historic building facades in urban renewal—taking Fujian Putian historic districts as an example. Buildings 13 (6), 1478.

[87] Liu, Y. , Luo, Y. , Deng, Q. , Zhou, X. , 2021. Exploration of campus layout based on generative adversarial network: discussing the significance of small amount sample learning for architecture. In: Proceedings of the 2020 DigitalFUTURES: the 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020). Springer, pp. 169-178.

[88] Liu, Y. , Zhang, Z. , Deng, Q. , 2022. Exploration on diversity generation of campus layout based on gan. In: The International Conference on Computational Design and Robotic Fabrication(CDRF 2022). Springer, pp. 233-243.

[89] Liu, Y. , Li, H. , Deng, Q. , Hu, K. , 2023. Diffusion probabilistic model assisted 3D form finding and design latent space exploration: a case study for Taihu stone spatial transformation. In: Proceedings of the 2023 DigitalFUTURES: the International Conference on Computational Design and Robotic Fabrication (CDRF 2023). Springer, pp. 11-23.

[90] Liu, J. , Qiu, Z. , Wang, L. , Liu, P. , Cheng, G. , Chen, Y. , 2024. Intelligent floor plan design of modular high-rise residential building based on graph-constrained generative adversarial networks. Autom. ConStruct. 159, 105264.

[91] Lu, X. , Liao, W. , Zhang, Y. , Huang, Y. , 2022. Intelligent structural design of shear wall residence using physics-enhanced generative adversarial networks. Earthq. Eng. Struct. Dynam. 51 (7), 1657- 1676.

[92] Luo, Z. , Huang, W. , 2022. Floorplangan: vector residential floorplan adversarial generation. Autom. ConStruct. 142, 104470.

[93] Luo, S. , Tan, Y. , Huang, L. , Li, J. , Zhao, H. , 2023. Latent consistency models: synthesizing high-resolution images with fewstep inference. arXiv preprint arXiv: 2310.04378.

[94] Mayrhofer-Hufnagl, I. , Ennemoser, B. , 2023a. From linear to manifold interpolation. Digital Design Reconsidered, Education and Research in Computer Aided Architectural Design in Europe(eCAADe) 2, 419- 429.

[95] Mayrhofer-Hufnagl, I. , Ennemoser, B. , 2023b. Advancing justice in a city's complex systems using designs enabled by space. Int. J. Architect. Comput. 21 (2), 280- 296.

[96] Meng, S. , 2022. Exploring in the latent space of design: a method of plausible building facades images generation, properties control and model explanation base on stylegan2. In: Proceedings of the 2021 DigitalFUTURES: the 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021). Springer, Singapore, pp. 55-68.

[97] Mescheder, L. , Oechsle, M. , Niemeyer, M. , Nowozin, S. , Geiger, A. , 2019. Occupancy networks: learning 3d reconstruction in function space. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), pp. 4455-4465.

[98] Mildenhall, B. , Srinivasan, P.P. , Tancik, M. , Barron, J.T. , Ramamoorthi, R. , Ng, R. , 2020. Nerf: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65 (1), 99- 106.

[99] Mildenhall, B. , Srinivasan, P.P. , Tancik, M. , Barron, J.T. , Ramamoorthi, R. , Ng, R. , 2021. Nerf: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65 (1), 99- 106.

[100] Min, X. , Zheng, L. , Chen, Y. , 2023. The floor plan design method of exhibition halls in cgan-assisted museum architecture. Buildings 13 (3), 756.

[101] Mirza, M. , Osindero, S. , 2014. Conditional generative adversarial nets. arXiv preprint arXiv: 1411.1784.

[102] Mostafavi, F. , Tahsildoost, M. , Zomorodian, Z.S. , Shahrestani, S.S. , 2022. An interactive assessment framework for residential space layouts using pix2pix predictive model at the early-stage building design. Smart and Sustainable Built Environment 13 (4), 809- 827.

[103] Nash, C. , Ganin, Y. , Eslami, S.A. , Battaglia, P. , 2020. Polygen: an autoregressive generative model of 3d meshes. In: International Conference on Machine Learning. PMLR, pp. 7220-7229.

[104] Nauata, N. , Chang, K.-H. , Cheng, C.-Y. , Mori, G. , Furukawa, Y. , 2020. House-gan: relational generative adversarial networks for graph-constrained house layout generation. In: Computer Vision—ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I 16. Springer, pp. 162-177.

[105] Nauata, N. , Hosseini, S. , Chang, K.-H. , Chu, H. , Cheng, C.-Y. , Furukawa, Y. , 2021. House-gan++: generative adversarial layout refinement network towards intelligent computational agent for professional architects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13632-13641.

[106] Navarro-Mateu, D. , Carrasco, O. , Cortes Nieves, P. , 2021. Colorpatterns to architecture conversion through conditional generative adversarial networks. Biomimetics 6 (1), 16.

[107] Newton, D. , 2019. Generative deep learning in architectural design. Technology— Architecture+ Design 3 (2), 176-189. Taylor & Francis.

[108] Nichol, A. , Dhariwal, P. , Ramesh, A. , Shyam, P. , Mishkin, P. , McGrew, B. , Sutskever, I. , Chen, M. , 2021. Glide: towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv: 2112.10741.

[109] Paananen, V. , Oppenlaender, J. , Visuri, A. , 2023. Using text-toimage generation for architectural design ideation. arXiv preprint arXiv: 2304.10182.

[110] Papamakarios, G. , Nalisnick, E. , Rezende, D.J. , Mohamed, S. , Lakshminarayanan, B. , 2021. Normalizing flows for probabilistic modeling and inference. arXiv preprint arXiv: 1912.02762.

[111] Para, W. , Guerrero, P. , Kelly, T. , Guibas, L.J. , Wonka, P. , 2021. Generative layout modeling using constraint graphs. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6690-6700.

[112] Park, J.J. , Florence, P. , Straub, J. , Newcombe, R. , Lovegrove, S. , 2019. Deepsdf: learning continuous signed distance functions for shape representation. In: arXiv Preprint arXiv: 1901.05103.

[113] Peng, Y. , Zhao, C. , Xie, H. , Fukusato, T. , Miyata, K. , 2023. Difffacesketch: high-fidelity face image synthesis with sketch-guided latent diffusion model. arXiv preprint arXiv: 2302.06908.

[114] Poole, B. , Jain, A. , Barron, J.T. , Mildenhall, B. , 2023. Dreamfusion: text-to-3d using 2d diffusion. the International Conference on Learning Representations (ICLR) in 2023. arXiv preprint arXiv: 2209.14988.

[115] Pouliou, P. , Horvath, A.-S. , Palamas, G. , 2023. Speculative hybrids: investigating the generation of conceptual architectural forms through the use of 3d generative adversarial networks. Int. J. Architect. Comput. 21 (2).

[116] Qi, C.R. , Su, H. , Mo, K. , Guibas, L.J. , 2017. Pointnet: deep learning on point sets for 3d classification and segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. CVPR), pp. 77-85.

[117] Qian, W. , Xu, Y. , Li, H. , 2022. A self-sparse generative adversarial network for autonomous early-stage design of architectural sketches. Comput. Aided Civ. Infrastruct. Eng. 37 (5), 612- 628.

[118] Qian, G. , Mai, J. , Hamdi, A. , Ren, J. , Siarohin, A. , Li, B. , Lee, H.-Y. , Skorokhodov, I. , Wonka, P. , Tulyakov, S. , Ghanem, B. , 2024. Magic123: one image to high-quality 3d object generation using both 2d and 3d diffusion priors. In: In arXiv Preprint arXiv: 2306.17843.

[119] Quan, S.J. , 2022. Urban-gan: an artificial intelligence-aided computation system for plural urban design. Environ. Plan. B Urban Anal. City Sci. 49 (9), 2500- 2515.

[120] Radford, A. , Narasimhan, K. , Salimans, T. , Sutskever, I. , et al., 2018. Improving language understanding by generative pretraining. arXiv preprint arXiv: 2304.04309.

[121] Radford, A. , Kim, J.W. , Hallacy, C. , Ramesh, A. , Goh, G. , Agarwal, S. , Sastry, G. , Askell, A. , Mishkin, P. , Clark, J. , et al., 2021. Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning (ICML), 139, pp. 8748-8763 arXiv preprint arXiv: 2103.00020.

[122] Ramesh, A. , Pavlov, M. , Goh, G. , Gray, S. , Voss, C. , Radford, A. , Chen, M. , Sutskever, I. , 2021. Zero-shot text-to-image generation. In: International Conference on Machine Learning. PMLR, pp. 8821-8831.

[123] Ramesh, A. , Dhariwal, P. , Nichol, A. , Chu, C. , Chen, M. , 2022. Hierarchical text-conditional image generation with clip latents. In: Proceedings of the 40th International Conference on Machine Learning (ICML), 202, pp. 12345-12356 arXiv preprint arXiv: 2204.06125.

[124] Rombach, R. , Blattmann, A. , Lorenz, D. , Esser, P. , Ommer, B. , 2022. High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684-10695.

[125] Ronneberger, O. , Fischer, P. , Brox, T. , 2015. U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, pp. 234-241.

[126] Royal Institute of British Architects , 2024. RIBA AI Report 2024. Royal Institute of British Architects.

[127] Sebestyen, A. , Hirschberg, U. , Rasoulzadeh, S. , 2023a. Using deep learning to generate design spaces for architecture. Int. J. Architect. Comput. 21 (2), 337- 357.

[128] Sebestyen, A. , Özdenizci, O. , Legenstein, R. , Hirschberg, U. , 2023b. Generating conceptual architectural 3d geometries with denoising diffusion models. In: 41st Conference on Education and Research in Computer Aided Architectural Design in EuropeDigital Design Reconsidered: eCAADe 2023, vol. 2, pp. 451-460.

[129] Seneviratne, S. , Senanayake, D. , Rasnayaka, S. , Vidanaarachchi, R. , Thompson, J. , 2022. Dalle-urban: capturing the urban design expertise of large text to image transformers. In: 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, pp. 1-9.

[130] Shabani, M.A. , Hosseini, S. , Furukawa, Y. , 2023. Housediffusion: vector floorplan generation via a diffusion model with discrete and continuous denoising. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5466-5475.

[131] Shum, K.C. , Pang, H.-W. , Hua, B.-S. , Nguyen, D.T. , Yeung, S.-K. , 2023. Conditional 360-degree image synthesis for immersive indoor scene decoration. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4478-4488.

[132] Singer, U. , Polyak, A. , Hayes, T. , Yin, X. , An, J. , Zhang, S. , Hu, Q. , Yang, H. , Ashual, O. , Gafni, O. , et al., 2022. Make-a-video: text-to-video generation without text-video data. In: Proceedings of the 11th International Conference on Learning Representations (ICLR), May 1-5, 2023 arXiv preprint arXiv: 2209.14792.

[133] Song, Y. , Ermon, S. , 2019. Generative modeling by estimating gradients of the data distribution. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS) arXiv preprint arXiv: 1907.05600.

[134] Sourek, M. , 2024. AI in architecture and engineering from misconceptions to game-changing prospects. Architectural Intelligence 3 (1), 4.

[135] Stigsen, M.B. , Moisi, A. , Rasoulzadeh, S. , Schinegger, K. , Rutzinger, S. , 2023. Ai diffusion as design vocabulary-investigating the use of ai image generation in early architectural design and education. In: Digital Design Reconsidered-Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023), pp. 587-596.

[136] Su, P. , Lu, W. , Chen, J. , Hong, S. , 2023. Floor plan graph learning for generative design of residential buildings: a discrete denoising diffusion model. Build. Res. Inf. 52 (6), 627- 643.

[137] Sukkar, A.W. , Fareed, M.W. , Yahia, M.W. , Abdalla, S.B. , Ibrahim, I. , Senjab, K.A.K. , 2024. Analytical evaluation of Midjourney Architectural Virtual Lab: defining major current limits in AIgenerated representations of Islamic architectural heritage. Buildings 14 (3), 786. MDPI.

[138] Sun, C. , Zhou, Y. , Han, Y. , 2022a. Automatic generation of architecture facade for historical urban renovation using generative adversarial network. Build. Environ. 212, 108781.

[139] Sun, X. , Wang, Y. , Zhang, T. , Wang, Y. , Fu, H. , Li, X. , Liu, Z. , 2022c. An application of deep neural network in facade planning of coastal city buildings. In: International Conference on Computer Science and its Applications and the International Conference on Ubiquitous Information Technologies and Applications. Springer, pp. 517-523.

[140] Sun, J. , Zhang, B. , Shao, R. , Wang, L. , Liu, W. , Xie, Z. , Liu, Y. , 2023a. Dreamcraft3d: hierarchical 3d generation with bootstrapped diffusion prior arXiv preprint arXiv: 2310.16818.

[141] Sun, P. , Yan, F. , He, Q. , Liu, H. , 2023b. The development of an experimental framework to explore the generative design preference of a machine learning-assisted residential site plan layout. Land 12 (9), 1776.

[142] Tan, L. , Luhrs, M. , 2024. Using generative AI Midjourney to enhance divergent and convergent thinking in an architect's creative design process. Des. J. 677-699. Taylor & Francis.

[143] Tanasra, H. , Rott Shaham, T. , Michaeli, T. , Austern, G. , Barath, S. , 2023. Automation in interior space planning: utilizing conditional generative adversarial network models to create furniture layouts. Buildings 13 (7), 1793.

[144] Taura, T. , Nagai, Y. , 2013. Concept generation for design creativity: a systematized theory and methodology. In: Concept Generation for Design Creativity: A Systematized Theory and Methodology, pp. 9-20.

[145] Thoppilan, R. , De Freitas, D. , Hall, J. , Shazeer, N. , Kulshreshtha, A. , Cheng, H.-T. , Jin, A. , Bos, T. , Baker, L. , Du, Y. , et al., 2022. Lamda: language models for dialog applications. arXiv preprint arXiv: 2201.08239.

[146] Tong, H. , Türel, A. , Şenkal, H. , Ergun, S. , Güzelci, O.Z. , Alaçam, S. , 2023. Can AI function as a new mode of sketching: a teaching experiment with freshman. International Journal of Emerging Technologies in Learning 18 (18).

[147] Tono, A. , Huang, H. , Agrawal, A. , Fischer, M. , 2022. Vitruvio: 3d building meshes via single perspective sketches arXiv preprint arXiv: 2210.13634.

[148] Uzun, C. , Çolakoǧlu, M.B. , Inceoǧlu, A. , 2020. Gan as a generative architectural plan layout tool: a case study for training dcgan with palladian plans and evaluation of dcgan outputs. A—Z ITU JOURNAL OF THE FACULTY OF ARCHITECTURE 17 (2), 185- 198.

[149] Vaswani, A. , Shazeer, N. , Parmar, N. , Uszkoreit, J. , Jones, L. , Gomez, A.N. , Kaiser, Ł. , Polosukhin, I. , 2017. Attention is all you need arXiv preprint arXiv: 1706.03762.

[150] Veloso, P. , Rhee, J. , Bidgoli, A. , De Guevara, M.L. , 2022. A pedagogical experience with deep learning for floor plan generation. In: POST-CARBON, Proceedings of the 27th International Conference of the Association for ComputerAided Architectural Design Research in Asia (CAADRIA) 2022, vol. 1, pp. 373-382.

[151] Vermisso, E. , 2022. Semantic ai models for guiding ideation in architectural design courses. In: Proceedings of the 13th International Conference on Computational Creativity, pp. 205-209.

[152] Veselỳ O. , 2022. Building massing generation using gan trained on Dutch 3d city models. In: Design Modelling Symposium Berlin, pp. 13-23.

[153] Voynov, A. , Babenko, A. , 2020. Unsupervised discovery of interpretable directions in the gan latent space. In: International Conference on Machine Learning. PMLR, pp. 9786-9796.

[154] Wakita, O.A. , Linde, R.M. , 2003. The Professional Practice of Architectural Working Drawings. John Wiley Sons Inc.

[155] Wan, D. , Zhao, R. , Zhang, S. , Liu, H. , Guo, L. , Li, P. , Ding, L. , 2023. A deep learning-based approach to generating comprehensive building façades for low-rise housing. Sustainability 15 (3), 1816.

[156] Wang, N. , Zhang, Y. , Li, Z. , Fu, Y. , Liu, W. , Jiang, Y.-G. , 2018. Pixel2mesh: generating 3d mesh models from single rgb images. In: Proceedings of the European Conference on Computer Vision(ECCV), pp. 52-67.

[157] Wang, C. , Chai, M. , He, M. , Chen, D. , Liao, J. , 2021a. Clip-nerf: text-and-image driven manipulation of neural radiance fields. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022 arXiv preprint arXiv: 2112.05139.

[158] Wang, S. , Zeng, W. , Chen, X. , Ye, Y. , Qiao, Y. , Fu, C.-W. , 2021b. Actfloor-gan: activity-guided adversarial networks for humancentric floorplan design. IEEE Trans. Visual. Comput. Graph. 29 (3), 1610- 1624.

[159] Wang, L. , Vincent, N. , Rukanskaite, J. , Zhang, A.X. , 2023. Pika: empowering non-programmers to author executable governance policies in online communities. arXiv preprint arXiv: 2310.04329.

[160] Wu, J. , Zhang, C. , Zhang, X. , Zhang, Z. , Freeman, W.T. , Tenenbaum, J.B. , 2018. Learning shape priors for single-view 3d completion and reconstruction. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 646-662.

[161] Xing, J. , Xia, M. , Zhang, Y. , Chen, H. , Wang, X. , Wong, T.-T. , Shan, Y. , 2023. Dynamicrafter: animating open-domain images with video diffusion priors. In: Proceedings of the European Conference on Computer Vision (ECCV) 2024, Lecture Notes in Computer Science, 13861, pp. 398-415 arXiv preprint arXiv: 2310.12190.

[162] Xu, Y.L.W. , 2023. Research on architectural sketch to scheme image based on context encoder. In: Proceedings of the 28th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2023, vol. 1, pp. 69-78.

[163] Xu, L. , Xiangli, Y. , Rao, A. , Zhao, N. , Dai, B. , Liu, Z. , Lin, D. , 2021. Blockplanner: city block generation with vectorized graph representation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5057-5066.

[164] Yang, L. , Li, L. , Chen, Q. , Zhang, J. , Feng, T. , Zhang, W. , 2023. Street layout design via conditional adversarial learning arXiv preprint arXiv: 2305.08186.

[165] Ye, X. , Du, J. , Ye, Y. , 2022. Masterplangan: facilitating the smart rendering of urban master plans via generative adversarial networks. Environ. Plan. B Urban Anal. City Sci. 49 (3), 794- 814.

[166] Yi, R. , Liu, Y.-J. , Lai, Y.-K. , Rosin, P.L. , 2019. Apdrawinggan: generating artistic portrait drawings from face photos with hierarchical gans. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10743-10752.

[167] Yu, Q. , Malaeb, J. , Ma, W. , 2020. Architectural facade recognition and generation through generative adversarial networks. In: 2020 International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), pp. 310-316. IEEE.

[168] Zeng, P. , Gao, W. , Yin, J. , Xu, P. , Lu, S. , 2024. Residential floor plans: multi-conditional automatic generation using diffusion models. Autom. ConStruct. 162, 105374.

[169] Zhang, H. , 2020. Text-to-form. In: Proceedings of the 40th Annual Conference of the Association for Computer Aided Design in Architecture, pp. 238-247.

[170] Zhang, L. , Agrawala, M. , 2023. Adding conditional control to textto-image diffusion models arXiv preprint arXiv: 2302.05543.

[171] Zhang, H. , Blasetti, E. , 2020. 3d architectural form style transfer through machine learning. In: Proceedings of the 25th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), pp. 659-668.

[172] Zhang, H. , Huang, Y. , 2021. Machine learning aided 2d-3d architectural form finding at high resolution. In: Proceedings of the 2020 DigitalFUTURES: the 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020). Springer, pp. 159-168.

[173] Zhang, H. , Xu, T. , Li, H. , Zhang, S. , Wang, X. , Huang, X. , Metaxas, D.N. , 2017. Stackgan: text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5907-5915.

[174] Zhang, L. , Zheng, L. , Chen, Y. , Huang, L. , Zhou, S. , 2022. Cganassisted renovation of the styles and features of street facades—a case study of the wuyi area in fujian, China. Sustainability 14 (24), 16575.

[175] Zhang, J. , Fukuda, T. , Yabki, N. , Li, Y. , 2023. Synthesizing stylesimilar residential facade from semantic labeling according to the user-provided example. In: HUMAN-CENTRIC, Proceedings of the 28th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2023, vol. 1, pp. 139-148.

[176] Zhang, Z. , Fort, J.M. , Mateu, L.G. , 2023. Exploring the potential of artificial intelligence as a tool for architectural design: a perception study using Gaudí's works. Buildings 13 (7), 1863. MDPI.

[177] Zhao, B. , Meng, L. , Yin, W. , Sigal, L. , 2019. Image generation from layout. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8576-8585.

[178] Zhao, C.-W. , Yang, J. , Li, J. , 2021. Generation of hospital emergency department layouts based on generative adversarial networks. J. Build. Eng. 43, 102539.

[179] Zhao, P. , Liao, W. , Xue, H. , Lu, X. , 2022. Intelligent design method for beam and slab of shear wall structure based on deep learning. J. Build. Eng. 57, 104838.

[180] Zhao, P. , Liao, W. , Huang, Y. , Lu, X. , 2023. Intelligent design of shear wall layout based on graph neural networks. Adv. Eng. Inf. 55, 101886.

[181] Zheng, H. , Yuan, P.F. , 2021. A generative architectural and urban design method through artificial neural networks. Build. Environ. 205, 108178.

[182] Zhong, C. , Yi'an Shi, L.H.C. , Wang, L. , 2024. AI-enhanced performative building design optimization and exploration. In: 29th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA, vol. 1, pp. 59-68.

[183] Zhou, S. , Wang, Y. , Jia, W. , Wang, M. , Wu, Y. , Qiao, R. , Wu, Z. , 2023. Automatic responsive-generation of 3d urban morphology coupled with local climate zones using generative adversarial network. Build. Environ. 245, 110855.

[184] Zhuang, X. , Design, C. , Phase, E. , Generative, C. , Network, A. , 2022. Rendering Sketches-interactive rendering generation from sketches using conditional generative adversarial neural network. In: 40th Education and Research in Computer Aided Architectural Design in Europe (eCAADe) Conference (eCAADe 2022), vol. 1, pp. 517-521.

[185] Zhuang, X. , Ju, Y. , Yang, A. , Caldas, L. , 2023. Synthesis and generation for 3d architecture volume with generative modeling. Int. J. Architect. Comput. 21 (2), 297- 314.

[186] Zhuang, J. , Li, G. , Xu, H. , Xu, J. , Tian, R. , 2024. TEXT-TO-CITY: controllable 3D urban block generation with latent diffusion model. In: Proceedings of the 29th International Conference of the Association for ComputerAided Architectural Design Research in Asia (CAADRIA) 2024, 196- 178.

RIGHTS & PERMISSIONS

The Author(s). Publishing services by Elsevier B.V. on behalf of Higher Education Press and KeAi.