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FROM SLICES TO SPACES Design ideation on architectural models through AI-generated image sequences.

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Title: FROM SLICES TO SPACES Design ideation on architectural models through AI-generated image sequences.
Authors: Bank, Mathias1 (AUTHOR) mathias.bank-stigsen@uibk.ac.at, Schlusche, Johannes1 (AUTHOR) johannes.schlusche@uibk.ac.at, Rasoulzadeh, Shervin2 (AUTHOR), Rutzinger, Stefan1 (AUTHOR), Schinegger, Kristina1 (AUTHOR)
Source: International Journal of Architectural Computing. Sep2025, Vol. 23 Issue 3, p621-639. 19p.
Subjects: Artificial intelligence, Architectural models, Design thinking, Spatial arrangement
Abstract: The paper presents a novel methodology for applying AI-driven style transfer to complex 3D architectural models. By converting 3D models into 2D image sequences, the process integrates sequential slicing, training, video-guided diffusion and reconstruction to transform existing 3D models based on text, image, or video prompts into new stylised forms. This enables architects to explore diverse design concepts, focusing on spatial composition, visual appearance and tectonics through high-resolution outputs that capture both exterior and interior spatial relations. The results demonstrates the setups potential in enhancing early-stage design ideation through AI, by both outperforming existing video diffusion platform while also facilitating a fast exploration of different outcomes - capabilities which were validated in a design course. The study highlights an approach for utilising advanced 2D image-based AI models to generate intricate and meaningful 3D architectural transformations. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Architectural Computing is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: FROM SLICES TO SPACES Design ideation on architectural models through AI-generated image sequences.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Architectural+Computing%22">International Journal of Architectural Computing</searchLink>. Sep2025, Vol. 23 Issue 3, p621-639. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Architectural+models%22">Architectural models</searchLink><br /><searchLink fieldCode="DE" term="%22Design+thinking%22">Design thinking</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+arrangement%22">Spatial arrangement</searchLink>
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  Data: The paper presents a novel methodology for applying AI-driven style transfer to complex 3D architectural models. By converting 3D models into 2D image sequences, the process integrates sequential slicing, training, video-guided diffusion and reconstruction to transform existing 3D models based on text, image, or video prompts into new stylised forms. This enables architects to explore diverse design concepts, focusing on spatial composition, visual appearance and tectonics through high-resolution outputs that capture both exterior and interior spatial relations. The results demonstrates the setups potential in enhancing early-stage design ideation through AI, by both outperforming existing video diffusion platform while also facilitating a fast exploration of different outcomes - capabilities which were validated in a design course. The study highlights an approach for utilising advanced 2D image-based AI models to generate intricate and meaningful 3D architectural transformations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Architectural Computing is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/14780771251352951
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        Text: English
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      – SubjectFull: Architectural models
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      – SubjectFull: Design thinking
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      – SubjectFull: Spatial arrangement
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              M: 09
              Text: Sep2025
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              Y: 2025
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