| id |
caadria2025_567 |
| authors |
Du, Xusheng, Gui, Ruihan, Wang, Zhengyang, Zhang, Ye and Xie, Haoran |
| year |
2025 |
| title |
Multi-View Depth Consistent Image Generation Using Generative AI Models: Application on architectural design of university buildings |
| source |
Dagmar Reinhardt, Nicolas Rogeau, Christiane M. Herr, Anastasia Globa, Jielin Chen, Taro Narahara (eds.), ARCHITECTURAL INFORMATICS - Proceedings of the 30th CAADRIA Conference, Tokyo, 22-29 March 2025, Volume 1, pp. 111–120 |
| summary |
In the early stages of architectural design, shoebox models are typically used as a simplified representation of building structures but require extensive operations to transform them into detailed designs. Generative artificial intelligence (AI) provides a promising solution to automate this transformation, but ensuring multi-view consistency remains a significant challenge. To solve this issue, we propose a novel three-stage consistent image generation framework using generative AI models to generate architectural designs from shoebox model representations. The proposed method enhances state-of-the-art image generation diffusion models to generate multi-view consistent architectural images. We employ ControlNet as the backbone and optimize it to accommodate multi-view inputs of architectural shoebox models captured from predefined perspectives. To ensure stylistic and structural consistency across multi-view images, we propose an image space loss module that incorporates style loss, structural loss and angle alignment loss. We then use depth estimation method to extract depth maps from the generated multi-view images. Finally, we use the paired data of the architectural images and depth maps as inputs to improve the multi-view consistency via the depth-aware 3D attention module. Experimental results demonstrate that the proposed framework can generate multi-view architectural images with consistent style and structural coherence from shoebox model inputs. |
| keywords |
Multi-view consistency, image generation, generative AI models, architectural design, university buildings |
| series |
CAADRIA |
| email |
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| full text |
 file.pdf (1,342,080 bytes) |
| references |
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| last changed |
2025/04/18 12:24 |
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