Abstract
As artificial intelligence (AI) methods advance quickly, more and more researchers are becoming interested in how to incorporate them into architectural design. Co-creation between humans and machines is also gaining popularity, which lends credence to the idea that AI can aid in the creative phases of design. The research presented in this article develops an AI-assisted method for generative design. It envisions a pipeline that iteratively kneads from semantics to two-dimensional (2D) images to three-dimensional (3D) models and back again by combining a semantic AI model (CLIP) with differentiable rendering. It also enables conceptual form exploration in Rhino3D with the help of a neural network built on a Text2Mesh tool. The real-time, conceptual, iterative interplay between human designers and AI collaborators could be facilitated by this pipeline. We also conducted a case study on early concept exploration for a museum to validate our approach, showcasing its potential in practical design scenarios.
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References
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical Text-Conditional Image Generation with CLIP Latents (2022)
Saharia, C., et al.: Photorealistic text-to-image diffusion models with deep language understanding (2022)
Wang, J.J.: Dall-e: creating images from text, 1–12 (2021)
Metz, T.: Building Meaning: An Architecture Studio Primer on Design, Theory, and History (2022)
Rezaei, M.: Reviewing design process theories: Discourses in architecture, urban design and planning theories (2021)
Suwa, M., Gero, J., Purcell, T.: Unexpected discoveries and S-invention of design requirements: important vehicles for a design process. Des. Stud. 21, 539–567 (2000). https://doi.org/10.1016/S0142-694X(99)00034-4
Daniel, Max, Jack, Thomas, Red, Sam, Nadir, Sebastian: Midjourney. https://www.midjourney.com/home/#about
Radford, A., et al.: Learning transferable visual models from natural language supervision (2021)
Esser, P., Rombach, R., Ommer, B.: Taming transformers for high-resolution image synthesis. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 12868–12878 (2021). https://doi.org/10.1109/CVPR46437.2021.01268
Ching, F.D.K.: Architecture - Form, Space & Order (2015)
Fawcett, P.: Architecture Design Notebook (2003)
Schön, D.A., Wiggins, G.: Kinds of seeing in designing. Creativity Innov. Manage. 1, 68–74 (1992). https://doi.org/10.1111/j.1467-8691.1992.tb00031.x
Bachman, L.R.: Two Spheres: Physical and Strategic Design in Architecture (2012)
Pan, J.Y., Yang, H.J., Duygulu, P., Faloutsos, C.: Automatic image captioning. In: 2004 IEEE International Conference on Multimedia and Expo (ICME), vol. 3, pp. 1987–1990 (2004). https://doi.org/10.1109/icme.2004.1394652
Frans, K., Soros, L.B., Witkowski, O.: CLIPDraw: exploring text-to-drawing synthesis through language-image encoders (2021)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2021)
Nguyen-Phuoc, T., Li, C., Yang, Y.L., Balaban, S.: Rendernet: a deep convolutional network for differentiable rendering from 3D shapes. In: Advances in Neural Information Processing Systems, 2018-Decem, pp. 7891–7901 (2018)
Chaillou, S.: Artificial Intelligence in Architecture: From Research to Practice (2022)
Haviland, W.A.: II. Architecture. Excavations in the West Plaza of Tikal, pp. 3–30 (2019). https://doi.org/10.9783/9781949057027-006
Eltarabishy, S.: Towards Data-Driven Design: Leveraging open data and deep learning in symbol spotting for furniture layout planning (2017)
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405–421. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_24
De Miguel RodrÃguez, J., Villafañe, M.E., PiÅ¡korec, L., Sancho Caparrini, F.: Generation of geometric interpolations of building types with deep variational autoencoders. Des. Sci. (2020). https://doi.org/10.1017/dsj.2020.31
Petrov, M., Wortmann, T.: Latent fitness landscapes - exploring performance within the latent space of post-optimization results. SimAUD 2021 (2021)
Michel, O., Bar-On, R., Liu, R., Benaim, S., Hanocka, R.: Text2Mesh: text-driven neural stylization for meshes (2021)
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Dai, S., Li, Y., Grace, K., Globa, A. (2023). Towards Human-AI Collaborative Architectural Concept Design via Semantic AI. In: Turrin, M., Andriotis, C., Rafiee, A. (eds) Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries. CAAD Futures 2023. Communications in Computer and Information Science, vol 1819. Springer, Cham. https://doi.org/10.1007/978-3-031-37189-9_5
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