Generative early architectural visualizations: incorporating architect’s style-trained models
Abstract This study introduces a novel approach to architectural visualization using generative artificial intelligence (AI), particularly emphasizing text-to-image technology, to remarkably improve the visualization process right from the initial design phase within the architecture, engineering, a...
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| Published in | Journal of computational design and engineering Vol. 11; no. 5; pp. 40 - 59 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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04.09.2024
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| Abstract | Abstract This study introduces a novel approach to architectural visualization using generative artificial intelligence (AI), particularly emphasizing text-to-image technology, to remarkably improve the visualization process right from the initial design phase within the architecture, engineering, and construction industry. By creating more than 10 000 images incorporating an architect’s personal style and characteristics into a residential house model, the effectiveness of base AI models. Furthermore, various architectural styles were integrated to enhance the visualization process. This method involved additional training for styles with low similarity rates, which required extensive data preparation and their integration into the base AI model. Demonstrated to be effective across multiple scenarios, this technique markedly enhances the efficiency and speed of production of architectural visualization images. Highlighting the vast potential of AI in design visualization, our study emphasizes the technology’s shift toward facilitating more user-centered and personalized design applications. |
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| AbstractList | Abstract This study introduces a novel approach to architectural visualization using generative artificial intelligence (AI), particularly emphasizing text-to-image technology, to remarkably improve the visualization process right from the initial design phase within the architecture, engineering, and construction industry. By creating more than 10 000 images incorporating an architect’s personal style and characteristics into a residential house model, the effectiveness of base AI models. Furthermore, various architectural styles were integrated to enhance the visualization process. This method involved additional training for styles with low similarity rates, which required extensive data preparation and their integration into the base AI model. Demonstrated to be effective across multiple scenarios, this technique markedly enhances the efficiency and speed of production of architectural visualization images. Highlighting the vast potential of AI in design visualization, our study emphasizes the technology’s shift toward facilitating more user-centered and personalized design applications. |
| Author | Cha, Seung Hyun Yoo, Youngjin Lee, Jin-Kook |
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| Snippet | Abstract This study introduces a novel approach to architectural visualization using generative artificial intelligence (AI), particularly emphasizing... |
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| Title | Generative early architectural visualizations: incorporating architect’s style-trained models |
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