TY - JOUR
T1 - AI-driven image generation for enhancing design in digital fabrication
T2 - 2nd International Satellite Conference on Visual Pattern Extraction and Recognition for Cultural Heritage Understanding, VIPERC 2023
AU - Fallacara, Giuseppe
AU - Fanti, Maria Pia
AU - Parisi, Fabio
AU - Parisi, Nicola
AU - Sangiorgio, Valentino
N1 - Publisher Copyright:
© 2023 CEUR-WS. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Artificial Intelligence (AI) technologies, such as deep learning and neural networks, are being widely used across various sectors with an unprecedented acceleration in recent years, particularly in the field of image generation. Indeed, such innovation is becoming a paradigm-shifting technology in the Architecture, Engineering, and Construction (AEC) sector specifically for the generation of highly detailed and visually compelling images of architectural projects. The AI algorithms, trained on vast datasets, enable users to automatically generate realistic representations of buildings, interiors, and urban landscapes starting from a text string. The potential of these technologies is significant, but the current literature lacks well-defined processes for effectively utilizing these techniques to achieve implementable projects. This paper proposes a novel design approach based on AI-driven Image Generation to support design for digital fabrication, consisting of three steps. Firstly, the conceptual design is defined along with a set of key-words. Secondly, the application of AI in image generation allows designers to efficiently explore a multitude of design possibilities. The AI-based tools facilitate the automatic generation of diverse design variants, aiding professionals in evaluating different options and enhancing their visualizations. Thirdly, by leveraging a synergistic set of techniques including image processing, 3D CAD design, and additive manufacturing, it is possible to transform the images suggested by AI into an actual project that can be effectively fabricated. Finally, the potential of the proposed approach is applied to the case of urban furnishings in historic city centers in southern Italy.
AB - Artificial Intelligence (AI) technologies, such as deep learning and neural networks, are being widely used across various sectors with an unprecedented acceleration in recent years, particularly in the field of image generation. Indeed, such innovation is becoming a paradigm-shifting technology in the Architecture, Engineering, and Construction (AEC) sector specifically for the generation of highly detailed and visually compelling images of architectural projects. The AI algorithms, trained on vast datasets, enable users to automatically generate realistic representations of buildings, interiors, and urban landscapes starting from a text string. The potential of these technologies is significant, but the current literature lacks well-defined processes for effectively utilizing these techniques to achieve implementable projects. This paper proposes a novel design approach based on AI-driven Image Generation to support design for digital fabrication, consisting of three steps. Firstly, the conceptual design is defined along with a set of key-words. Secondly, the application of AI in image generation allows designers to efficiently explore a multitude of design possibilities. The AI-based tools facilitate the automatic generation of diverse design variants, aiding professionals in evaluating different options and enhancing their visualizations. Thirdly, by leveraging a synergistic set of techniques including image processing, 3D CAD design, and additive manufacturing, it is possible to transform the images suggested by AI into an actual project that can be effectively fabricated. Finally, the potential of the proposed approach is applied to the case of urban furnishings in historic city centers in southern Italy.
KW - AI-driven Image Generation
KW - Digital Fabrication
KW - Historic City Centres
KW - Technical Architecture
KW - Urban Furnishings
UR - http://www.scopus.com/inward/record.url?scp=85182025631&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85182025631
SN - 1613-0073
VL - 3600
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
Y2 - 25 September 2023 through 26 September 2023
ER -