생성형 AI 기반 초기설계단계 외관디자인 시각화 접근방안

• 영문명: Generative AI-based Exterior Building Design Visualization Approach in the Early Design Stage: Leveraging Architects’ Style-trained Models
• 발행기관: 한국BIM학회
• 저자명: 유영진(Youngjin Yoo) 이진국(Jin-Kook Lee)
• 간행물 정보: 『KIBIM Magazine』14권 2호, 13~24쪽, 전체 12쪽
• 주제분류: 공학 > 건축공학
• 파일형태: PDF
• 발행일자: 2024.06.30

국문 초록

This research suggests a novel visualization approach utilizing Generative AI to render photorealistic architectural alternatives images in the early design phase. Photorealistic rendering intuitively describes alternatives and facilitates clear communication between stakeholders. Nevertheless, the conventional rendering process, utilizing 3D modelling and rendering engines, demands sophisticate model and processing time. In this context, the paper suggests a rendering approach employing the text-to-image method aimed at generating a broader range of intuitive and relevant reference images. Additionally, it employs an Text-to-Image method focused on producing a diverse array of alternatives reflecting architects’ styles when visualizing the exteriors of residential buildings from the mass model images. To achieve this, fine-tuning for architects’ styles was conducted using the Low-Rank Adaptation (LoRA) method. This approach, supported by fine-tuned models, allows not only single style-applied alternatives, but also the fusion of two or more styles to generate new alternatives. Using the proposed approach, we generated more than 15,000 meaningful images, with each image taking only about 5 seconds to produce. This demonstrates that the Generative AI-based visualization approach significantly reduces the labour and time required in conventional visualization processes, holding significant potential for transforming abstract ideas into tangible images, even in the early stages of design.

목차

1. 서론

2. 이론적 고찰

3. 생성형 AI 기반 시각화

4. 생성형 AI 기반 건축 외관 시각화 방안

5. 결론

감사의 글

References

키워드

건축 시각화 건축가 스타일 스타일 조합 생성형 AI 디퓨전 모델 Architectural Visualization Architect’s Style Style Fusion Generative AI Diffusion Models