Artificial intelligence turns simple text into realistic building designs

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The figure shows how the proposed framework turns a text description of a building into a realistic architectural image, step by step. First, the system uses the text prompt to generate a simple structural sketch that captures the overall shape of the building, including the correct number of floors. Next, the sketch is refined by adding detailed architectural elements, such as windows and doors, using a database of real building components as a reference. Finally, the refined sketch is combined with the original text description to produce a high-quality, realistic building image that matches the designer’s intent.

Credit: Associate Professor Haoran Xie from the Japan Advanced Institute of Science and Technology

When working on projects, architects must quickly turn rough concepts into visual representations. Text-to-image models offer an opportunity in this field, where high-quality designs can be generated simply by typing a description. Some of these systems can also incorporate rough sketches or depth information, offering additional control over the results. However, these models often fail to generate accurate representations of the prompt. For example, even a direct prompt such as “generate a 5-story building” might result in an image of a building with the incorrect number of floors. The reason lies in the training datasets, which lack detailed annotations about building structure, making it difficult for artificial intelligence (AI) to understand precise spatial requirements, such as floor counts or…

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