Cumincad : CUMINCAD Papers : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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_id	ecaade2023_436
id	ecaade2023_436
authors	Bank Stigsen, Mathias, Moisi, Alexandra, Rasoulzadeh, Shervin, Schinegger, Kristina and Rutzinger, Stefan
year	2023
title	AI Diffusion as Design Vocabulary - Investigating the use of AI image generation in early architectural design and education
doi	https://doi.org/10.52842/conf.ecaade.2023.2.587
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 587–596
summary	This paper investigates the potential of Text-to-Image AI in assisting the ideation phase in architectural design and education. The study proposes a structured workflow and tests it with first-year architecture students. It aims to create a comprehensive design vocabulary by using AI-generated images as primary design references and incorporating them into a modelling workflow. The paper implements a process combining specific vocabulary extraction, image generation, 2D to 3D translation, and spatial composition within a six weeklong design course. The findings suggest that such a process can enhance the ideation phase by generating new and diverse design inspirations, improve spatial understanding through the exploration of various design elements, and provide students with a targeted visual vocabulary that helps define design intention and streamlines the modelling process.
keywords	Artificial Intelligence, Text-to-Image, Midjourney, Architectural design, Design ideation, 2D to 3D
series	eCAADe
email
last changed	2023/12/10 10:49

_id	caadria2023_446
id	caadria2023_446
authors	Guida, George
year	2023
title	Multimodal Architecture: Applications of Language in a Machine Learning Aided Design Process
doi	https://doi.org/10.52842/conf.caadria.2023.2.561
source	Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 561–570
summary	Recent advances in Natural Language Processing (NLP) and Diffusion Models (DMs) are leading to a significant change in the way architecture is conceived. With capabilities that surpass those of current generative models, it is now possible to produce an unlimited number of high-quality images (Dhariwal and Nichol 2021). This opens up new opportunities for using synthetic images and marks a new phase in the creation of multimodal 3D forms, central to architectural concept design stages. Presented here are three methodologies of generation of meaningful 2D and 3D designs, merging text-to-image diffusion models Stable Diffusion, and DALL-E 2 with computational methods. These allow designers to intuitively navigate through a multimodal feedback loop of information originating from language and aided by artificial intelligence tools. This paper contributes to our understanding of machine-augmented design processes and the importance of intuitive user interfaces (UI) in enabling new dialogues between humans and machines. Through the creation of a prototype of an accessible UI, this exchange of information can empower designers, build trust in these tools, and increase control over the design process.
keywords	Machine Learning, Diffusion Models, Concept Design, Semantics, User Interface, Design Agency
series	CAADRIA
email
last changed	2023/06/15 23:14

_id	cdrf2023_24
id	cdrf2023_24
authors	Haoran Ma, Hao Zheng
year	2023
title	Text Semantics to Image Generation: A Method of Building Facades Design Base on Stable Diffusion Model
doi	https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_3
source	Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023)
summary	Stable Diffusion model has been extensively employed in the study of architectural image generation, but there is still an opportunity to enhance in terms of the controllability of the generated image content. A multi-network combined text-to-building facade image generating method is proposed in this work. We first fine-tuned the Stable Diffusion model on the CMP Facades dataset using the LoRA (Low-Rank Adaptation) approach, then we apply the ControlNet model to further control the output. Finally, we contrasted the facade generating outcomes under various architectural style text contents and control strategies. The results demonstrate that the LoRA training approach significantly decreases the possibility of fine-tuning the Stable Diffusion large model, and the addition of the ControlNet model increases the controllability of the creation of text to building facade images. This provides a foundation for subsequent studies on the generation of architectural images.
series	cdrf
email
last changed	2024/05/29 14:04

_id	ecaade2023_68
id	ecaade2023_68
authors	Mugita, Yuki, Fukuda, Tomohiro and Yabuki, Nobuyoshi
year	2023
title	Future Landscape Visualization by Generating Images Using a Diffusion Model and Instance Segmentation
doi	https://doi.org/10.52842/conf.ecaade.2023.2.549
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 549–558
summary	When designing a new landscape, such as when demolishing buildings and building new ones, visual methods are effective in sharing a common image. It is possible to visualize future landscapes by making sketches and models, but this requires a great deal of skill and effort on the part of the creator. One method for visualizing future landscapes without the need for specialized skills or labor is image generation using deep learning, and a method has been proposed of using deep learning to generate landscape images after demolishing current buildings. However, there are two problems: the inability to remove arbitrary buildings and the inability to generate a landscape after reconstruction. Therefore, this study proposes a future landscape visualization method that integrates instance segmentation and a diffusion model. The proposed method can generate both post-removal images of existing buildings and post-reconstruction images based on text input, without the need for specialized technology or labor. Verification results confirmed that the post-removal image was more than 90% accurate when the building was removed and replaced with the sky. And the post-reconstruction image matched the text content with a best accuracy of more than 90%. This research will contribute to the realization of urban planning in which all project stakeholders, both professionals and the public, can directly participate by visualizing their own design proposals for future landscapes.
keywords	landscape visualization, deep learning, diffusion model, instance segmentation, text input, text-to-image model, inpainting
series	eCAADe
email
last changed	2023/12/10 10:49

_id	acadia23_v2_582
id	acadia23_v2_582
authors	Wu, Kaicong; Li, Chenming; Su, Wenjun
year	2023
title	The Chair Game Experiment: Transforming Multiplayer Design Processes with Text-to-Image Generation and 2D-to-3D Modelling
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 2: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-0-3]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 582-595.
summary	The urgency for an inclusive architectural design process in conceptualizing the built environment stems from the need to establish effective communication between under- represented groups and design professionals. However, various challenges hinder the development of an inclusive design process that accommodates diverse stakeholders. Individual designers or selective design teams are frequently limited by their own visions, causing them to potentially overlook alternative solutions. Moreover, stakeholders who lack professional training might struggle to articulate their expectations. The emergence of generative AI (artificial intelligence) technologies has significantly reduced the tech- nical barriers in design, and has empowered non-professionals to vividly express their ideas regarding forms and spaces. This has presented a valuable opportunity to better understand the perspectives of underrepresented groups through visual representations. Therefore, this research aims to explore the impact of image generation on the democ- ratization of the design process. Using chair design as a testing ground, we propose an evolutionary computing framework that simulates interactions among designers and participants empowered by emerging AI technologies. To investigate the potential impact of image generation, we have implemented a multiplayer design game to allow computing agents to compete in exploring 3D chair forms. Through this approach, we aim to gain insights into how image generation influences design decisions, whether it generates more diversified solutions, and what values could be introduced into the built environment.
series	ACADIA
type	paper
email
last changed	2024/12/20 09:13

_id	ecaade2023_444
id	ecaade2023_444
authors	Gan, Amelia Wen Jiun, Dang, Quoc, Western, Blaine and García del Castillo, Jose Luis
year	2023
title	AI-Mediated Group Ideation
doi	https://doi.org/10.52842/conf.ecaade.2023.2.389
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 389–398
summary	Design charrettes and town hall formats are commonly used in the field of architecture to facilitate group ideation at multiple stages across a variety of stakeholders. Group ideation is critical to generate a wide range of solutions while covering all aspects of a defined problem. However, the format of group ideation often poses a multitude of challenges, including a lack of diverse ideation, difficulties in reaching consensus, imbalanced power dynamics, as well as maintaining focus throughout a group session. This paper explores how recent developments in AI frameworks could be utilized and assembled as a creative mediator in an architectural ideation process. The paper describes a framework and digital interface for AI-mediated group ideation where recent advancements in speech recognition, Natural Language Processing and Text-to-Image generation are leveraged to facilitate brainstorming processes. The paper first delves into the design of the framework and digital interface, taking into account in-person, remote and hybrid contexts, followed by the technical workflow and pilot evaluation methods used in this study. The resulting design is informed by AI-Mediated Communication, group dynamics and behavioral theories, along with core User Experience principles. The result takes the form of a visual ideation and transcription tool that allows users to ideate across conversational and visual methods.
keywords	AI-Mediated Communication, Ideation, Design Thinking, Natural Language Processing, Human-Computer Interaction
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ecaade2023_328
id	ecaade2023_328
authors	Andreou, Alexis, Kontovourkis, Odysseas, Solomou, Solon and Savvides, Andreas
year	2023
title	Rethinking Architectural Design Process using Integrated Parametric Design and Machine Learning Principles
doi	https://doi.org/10.52842/conf.ecaade.2023.2.461
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 461–470
summary	Artificial Intelligence (AI) has the potential to process vast amounts of subjective and conflicting information in architecture. However, it has mostly been used as a tool for managing information rather than as a means of enhancing the creative design process. This work proposes an innovative way to enhance the architectural design process by incorporating Machine Learning (ML), a type of Artificial Intelligence (AI), into a parametric architectural design process. ML would act as a mediator between the architects' inputs and the end-users' needs. The objective of this work is to explore how Machine Learning (ML) can be utilized to visualize creative designs by transforming information from one form to another - for instance, from text to image or image to 3D architectural shapes. Additionally, the aim is to develop a process that can generate comprehensive conceptual shapes through a request in the form of an image and/or text. The suggested method essentially involves the following steps: Model creation, Revisualization, Performance evaluation. By utilizing this process, end-users can participate in the design process without negatively affecting the quality of the final product. However, the focus of this approach is not to create a final, fully-realized product, but rather to utilize abstraction and processing to generate a more understandable outcome. In the future, the algorithm will be improved and customized to produce more relevant and specific results, depending on the preferences of end-users and the input of architects.
keywords	End-users, Architects, Mass personalization, Visual programming, Neural Network Algorithm
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ecaade2023_137
id	ecaade2023_137
authors	Blaas, Quintin, Pelosi, Antony and Brown, Andre
year	2023
title	Reconsidering Artificial Intelligence as Co-Designer
doi	https://doi.org/10.52842/conf.ecaade.2023.2.559
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 559–566
summary	The research in this paper is presented from the perspective of a designer interested in investigating using artificial intelligence, specifically machine learning, to act as a co-pilot during architectural design phases. Significant recent interest has been evident in, for instance, rapidly developing text-to-image and intelligent chat AI areas. However, we have a particular focus and have undertaken a series of feasibility experiments to explore the potential for enabling a designer's exploitation of machine learning, and consequently in effect, using machine learning as a co-designer. We conclude that the industry would need to develop certain protocols to take advantage of the opportunities available through such an AI-assisted approach.
keywords	Artificial Intelligence, Design Data, Algorithmic Design, Design Process, Co-Designing
series	eCAADe
email
last changed	2023/12/10 10:49

_id	sigradi2023_90
id	sigradi2023_90
authors	Codarin, Sara and Daubmann, Karl
year	2023
title	Rom[AI]nterrotta
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 705–716
summary	This contribution presents the outcomes of a three-credit elective course offered at Lawrence Technological University’s College of Architecture and Design that involved a week-long travel experience in Rome with undergraduate and graduate students. The course used on-the-fly digital synthetic creations with AI text-to-image and image-to-image generation. The students collectively produced a disciplinary design-fiction tour book for a futuristic Rome, integrated into the city's historical layers. Inspired by the 1978 Roma Interrotta/Interrupted Rome project, the students reimagined the city using AI-informed storytelling to create altered narratives that explored common themes and critical insights. The digital tools allowed students to seamlessly blend AI-generated ideas with photos from the tour, linking historical contexts and contemporary design proposals. The critical use of AI served as a valuable tool in this process, educating designers on the importance of site-specific considerations and capturing the essence of a place through innovative creations informed by their experiences.
keywords	AI, Text-to-Image, Storytelling, Travel Experience, Rome
series	SIGraDi
email
last changed	2024/03/08 14:07

_id	ecaade2023_145
id	ecaade2023_145
authors	Dortheimer, Jonathan, Schubert, Gerhard, Dalach, Agata, Brenner, Lielle Joy and Martelaro, Nikolas
year	2023
title	Think AI-side the Box! Exploring the Usability of text-to-image generators for architecture students
doi	https://doi.org/10.52842/conf.ecaade.2023.2.567
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 567–576
summary	This study examines how architecture students use generative AI image generating models for architectural design. A workshop was conducted with 25 participants to create designs using three state-of-the-art generative diffusion models and BIM or 3D modeling software. Results showed that the participants found the image-generating models useful for the preliminary design stages but had difficulty when the design advanced because the models did not perform as they expected. Finally, the study shows areas for improvement that merit further research. The paper provides empirical evidence on how generative diffusion models are used in an architectural context and contributes to the field of digital design.
keywords	Machine Learning, Diffusion Models, Design Process, Computational Creativity
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ijac202321202
id	ijac202321202
authors	Koehler, Daniel
year	2023
title	More than anything: Advocating for synthetic architectures within large-scale language-image models
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 242–255
summary	Large-scale language-image (LLI) models have the potential to open new forms of critical practice through architectural research. Their success enables designers to research within discourses that are profoundly connected to the built environment but did not previously have the resources to engage in spatial research. Although LLI models do not generate coherent building ensembles, they offer an esthetic experience of an AI infused design practice. This paper contextualizes diffusion models architecturally. Through a comparison of approaches to diffusion models in architecture, this paper outlines data-centric methods that allow architects to design critically using computation. The design of text-driven latent spaces extends the histories of typological design to synthetic environments including non-building data into an architectural space. More than synthesizing quantic ratios in various arrangements, the architect contributes by assessing new categorical differences into generated work. The architects’ creativity can elevate LLI models with a synthetic architecture, nonexistent in the data sets the models learned from.
keywords	diffusion models, large-scale language-image models, data-centric, access to data, discrete computation, critical computational practice, synthetic architecture
series	journal
last changed	2024/04/17 14:30

_id	sigradi2023_416
id	sigradi2023_416
authors	Machado Fagundes, Cristian Vinicius, Miotto Bruscato, Léia, Paiva Ponzio, Angelica and Chornobai, Sara Regiane
year	2023
title	Parametric environment for internalization and classification of models generated by the Shap-E tool
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1689–1698
summary	Computing has been increasingly employed in design environments, primarily to perform calculations and logical decisions faster than humans could, enabling tasks that would be impossible or too time-consuming to execute manually. Various studies highlight the use of digital tools and technologies in diverse methods, such as parametric modeling and evolutionary algorithms, for exploring and optimizing alternatives in architecture, design, and engineering (Martino, 2015; Fagundes, 2019). Currently, there is a growing emergence of intelligent models that increasingly integrate computers into the design process. Demonstrating great potential for initial ideation, artificial intelligence (AI) models like Shap-E (Nichol et al., 2023) by OpenAI stand out. Although this model falls short of state-of-the-art sample quality, it is among the most efficient orders of magnitude for generating three-dimensional models through AI interfaces, offering practical balance for certain use cases. Thus, aiming to explore this gap, the presented study proposes an innovative design agency framework by employing Shap-E connected with parametric modeling in the design process. The generation tool has shown promising results; through generations of synthetic views conditioned by text captions, its final output is a mesh. However, due to the lack of topological information in models generated by Shap-E, we propose to fill this gap by transferring data to a parametric three-dimensional surface modeling environment. Consequently, this interaction's use aims to enable the transformation of the mesh into quantifiable surfaces, subject to collection and optimization of dimensional data of objects. Moreover, this work seeks to enable the creation of artificial databases through formal categorization of parameterized outputs using the K-means algorithm. For this purpose, the study methodologically orients itself in a four-step exploratory experimental process: (1) creation of models generated by Shap-E in a pressing manner; (2) use of parametric modeling to internalize models into the Grasshopper environment; (3) generation of optimized alternatives using the evolutionary algorithm (Biomorpher); (4) and classification of models using the K-means algorithm. Thus, the presented study proposes, through an environment of internalization and classification of models generated by the Shap-E tool, to contribute to the construction of a new design agency methodology in the decision-making process of design. So far, this research has resulted in the generation and classification of a diverse set of three-dimensional shapes. These shapes are grouped for potential applications in machine learning, in addition to providing insights for the refinement and detailed exploration of forms.
keywords	Shap-E, Parametric Design, Evolutionary Algorithm, Synthetic Database, Artificial Intelligence
series	SIGraDi
email
last changed	2024/03/08 14:09

_id	ascaad2023_134
id	ascaad2023_134
authors	Salman, Huda; Dounas, Theodoros; Clarke, Connor
year	2023
title	Fluency of Creative Ideas in the Digital Age: Exploring Emergent AI Influences on Design Methodology and Visual Thinking in Architectural Education
source	C+++: Computation, Culture, and Context – Proceedings of the 11th International Conference of the Arab Society for Computation in Architecture, Art and Design (ASCAAD), University of Petra, Amman, Jordan [Hybrid Conference] 7-9 November 2023, pp. 815-832.
summary	Research has explored the concept of originality in visual thinking and architectural education, using different methods. The new state of Artificial Intelligence (AI) in architectural design represents another shift from traditional modes of architectural design and education, into a more authentic approach to the digital age. An experiment is designed to highlight the originality of this approach in design thinking and its futuristic trends and impact on education and creativity studies. The intent of the study we present here is twofold: one to revisit key design studies of design exploration and secondly to explore students' design activity while interacting with text-to-image diffusion machine learning (ML) generative models such as Midjourney, DALL-E and Stable Diffusion, as these might have the potential to change the way that architectural students approach the concept stages of designing projects and products. In addition, we are interested in how the new shift in interfaces and modes of stimulus will influence the students' design process and perceptions. Participants in the design process are final year students who had spent at least four years in a school of architecture and can be classified as semi-experienced designers. Further within the evaluation also lies a critique of the diffusion ML tools themselves as producers of architectonic images, rather than complete concepts for architecture that encapsulate spatial, formal, structural arrangements of elements.
series	ASCAAD
email
last changed	2024/02/13 14:41

_id	ecaade2023_250
id	ecaade2023_250
authors	Sebestyen, Adam, Özdenizci, Ozan, Hirschberg, Urs and Legenstein, Robert
year	2023
title	Generating Conceptual Architectural 3D Geometries with Denoising Diffusion Models
doi	https://doi.org/10.52842/conf.ecaade.2023.2.451
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 451–460
summary	Generative deep learning diffusion models have been attracting mainstream attention in the field of 2D image generation. We propose a prototype which brings a diffusion network into the third dimension, with the purpose of generating geometries for conceptual design. We explore the possibilities of generating 3D datasets, using parametric design to overcome the problem of the current lack of available architectural 3D data suitable for training neural networks. Furthermore, we propose a data representation based on volumetric density grids which is applicable to train diffusion networks. Our early prototype demonstrates the viability of the approach and suggests future options to develop deep learning generative 3D tools for architectural design.
keywords	Artificial Intelligence, Generative Deep Learning, Neural Networks, Diffusion Models, Parametric Design, 3D Data Representations
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ecaade2023_65
id	ecaade2023_65
authors	Sopher, Hadas, Casakin, Hernan and Gero, John S.
year	2023
title	The Temporal Effect of Immersive VR on Student-Tutor Interaction in Architectural Design Crits
doi	https://doi.org/10.52842/conf.ecaade.2023.1.191
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 1, Graz, 20-22 September 2023, pp. 191–200
summary	Using Immersive Virtual Reality (IVR) as a representational medium in architecture studio crits allows students and tutors to interact as they navigate in a life-scale digital display of the design, making these media relevant for the design learning process. Crits form a core component in design learning, as the setting fosters the interaction responsible for engaging students in practicing design behaviors and adopting the behaviors performed by tutors. The interaction changes throughout the crit and during the semester’s phases, to foster different pedagogical aims encapsulated in the learning process. Although extant research found IVRs supportive of studio crits, the IVRs’ temporal effect on student-tutor interaction is understudied, restricting the ways in which IVRs can be integrated to enhance the learners’ engagement. To this aim, verbalizations recorded in four early, and mid-semester crits using IVR and non-immersive (NI) media were analyzed using protocol analysis techniques to code new ideas and design issues generated during the interaction and analyze the cumulative distribution of issues generated for each medium throughout three crit phases. Findings show large differences between the tutor and the student. The IVR supported enhanced student engagement in ideation and the generation of issues, during the first and second crit phases, providing a positive indicator of the medium’s temporal support in the learning process. The methods used in this study provide knowledge for conducting further research to unfold how design is learned and taught
keywords	Immersive VR, Studio, Learning process, Learner engagement, Design cognition, Design ideation
series	eCAADe
email
last changed	2023/12/10 10:49

_id	sigradi2023_356
id	sigradi2023_356
authors	Wojcickoski, Vagner and Osterkamp, Guilherme
year	2023
title	Application of the “Double-Layered Model” Concept for the Use of AI in the Atelier
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 1477–1488
summary	The objective of this article is to present the application of the "Double-Layered Model" concept for image generation using AI tools in a design studio. To delimit this study, a workshop was conducted in a controlled environment, along with utilizing control protocols and analyzing the results produced in an undergraduate Architecture course in Brazil. The discipline challenges students to develop an urban park with the aim of illustrating future realities. The analysis of the process and the generated results allowed for an evaluation of the potential for generating images through an AI system, in light of concepts described by Goldschmidt. The students were guided to create images using inputs related to the concept. The variability of results based on the propositions for the design problem, combined with the guidance provided by the applied concept, suggests a potential supportive tool for generating ideas in a design studio.
keywords	Artificial Intelligence, Text-to-Image Generator, Landscape, Design Studio, Double-Layered Model.
series	SIGraDi
email
last changed	2024/03/08 14:08

_id	ecaade2023_48
id	ecaade2023_48
authors	Doumpioti, Christina and Huang, Jeffrey
year	2023
title	Text to Image to Data
doi	https://doi.org/10.52842/conf.ecaade.2023.2.541
source	Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 541–548
summary	Recent advancements in text-to-image technology have transformed the landscape of computational creativity by enabling the generation of conceptual images. By implementing innovative standards for image generation, we can now shift our focus from the constraints of notational design communication to more purposeful reflection, opening up new design possibilities for future architectures informed by contemporary ideas, concepts, and concerns. In light of the pressing climatic crisis, this paper specifically explores the relationship between text-to-image generation and the integration of environmental sensibility, aiming to explore how digital information (bits) can translate into physical reality (atoms). Our case study focuses on specific residential building typology and its façade morphology to analyse the environmental responsiveness of the design. We propose a workflow that merges creative and analytic processes, through different stages, including diffusion-generated conceptual images, 2D to 3D through depth-mapping and point-cloud meshing, semantic segmentation analysis and sunlight simulation. The paper describes the methods and their combination into a coherent workflow, outlines encountered setbacks, and suggests stages for further improvement.
keywords	Computational Creativity, Text-to-Image, Simulation, Environmental Responsiveness, Machine Intelligence
series	eCAADe
email
last changed	2023/12/10 10:49

_id	ascaad2023_071
id	ascaad2023_071
authors	Gabr, Rana
year	2023
title	Exploring the Integration of Mid-Journey AI for Architectural Post-Occupancy Evaluation - Prioritizing Experiential Assessment: Case Study of the Egyptian Museum and National Museum of Egyptian Civilization
source	C+++: Computation, Culture, and Context – Proceedings of the 11th International Conference of the Arab Society for Computation in Architecture, Art and Design (ASCAAD), University of Petra, Amman, Jordan [Hybrid Conference] 7-9 November 2023, pp. 518-550.
summary	The post-occupancy evaluation (POE) process, traditionally reliant on subjective user feedback and observations, has evolved in response to global concerns like climate change and sustainability. This shift towards greater objectivity and quantification reflects an increased focus on precise measurement of environmental and performance metrics. Consequently, architectural assessment is now more quantitatively oriented, moving away from a predominantly experiential emphasis. This research investigates the integration of the emerging AI tool Mid-journey into the POE process, specifically targeting the evaluation of experiential aspects in architectural design. It proposes that AI tools can be instrumental for architects and evaluators by translating user feedback into visual representations and conceptual insights. The study aims to initiate a discourse on the role of text-to-image models in assessing user experiences, potentially becoming integral to the design and concept generation process. The research combines quantitative methods like surveys and AI-driven experiments with qualitative approaches such as observations and interviews to offer enhancement proposals for Egyptian museums, comparing traditional POE solutions and frameworks with new proposed framework that incorporates AI-generated alternatives. This study emphasizes the dual role of museums as artifact custodians and platforms for public education about ancient cultures. It highlights the imperative to transform Egyptian national museums into immersive learning environments, rather than mere storage spaces. The aspiration is to create museums that securely display and preserve artifacts while fostering educational engagement in preserving our shared history.
series	ASCAAD
email
last changed	2024/02/13 14:40

_id	acadia23_v2_420
id	acadia23_v2_420
authors	Guida, George; Escobar, Daniel; Navarro, Carlos
year	2023
title	3D Neural Synthesis: Gaining Control with Neural Radiance Fields
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 2: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-0-3]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 420-428.
summary	This research introduces a novel, 3D machine-learning, aided design approach for early design stages. Integrating language within a multimodal framework grants designers greater control and agency in generating 3D forms. The proposed method leverages Stable Diffusion and Runway's Gen1 through the generation of 3D Neural Radiance Fields (NeRFs), surpassing the limitations of 2D image-based outcomes in aiding the design process. This paper presents a flexible machine-learning workflow taught to students in a conference workshop, and outlines the multimodal methods used - between text, image, video, and NeRFs. The resultant NeRF design outcomes are contextualized within a Unity agent-based, virtual environment for architectural simulation, and are expe- rienced with real-time VFX augmentations. This hybridized design process ultimately highlights the importance of feedback loops and control within machine-learning, aided-design processes.
series	ACADIA
type	paper
email
last changed	2024/12/20 09:12

_id	sigradi2023_114
id	sigradi2023_114
authors	Huang, Sheng-Yang, Wang, Yuankai and Jiang, Qingrui
year	2023
title	(In)Visible Cities: Exploring generative artificial intelligence'screativity through the analysis of a conscious journey in latent space
source	García Amen, F, Goni Fitipaldo, A L and Armagno Gentile, Á (eds.), Accelerated Landscapes - Proceedings of the XXVII International Conference of the Ibero-American Society of Digital Graphics (SIGraDi 2023), Punta del Este, Maldonado, Uruguay, 29 November - 1 December 2023, pp. 717–728
summary	The rise of generative AI has redefined architectural design by introducing latent space, challenging traditional methods. This paper aims to explore, structure, and analyse latent journeys, drawing from analytical design discourses. We construct journeys towards 'Isaura' from 'Invisible Cities' by Italo Calvino, bridging literature and visual narratives, utilising the text-image generating software, Midjourney. The objective is to identify spatial configurations that align with the designer's interpretation of the text, ensuring the accuracy of visual elements. Structured as a Markov (stochastic) process, the experiment encompasses four primary stages to offer a rational explanation for the journey and the role of each segment. Findings emphasise the potential of latent space in augmenting architectural design and underscore the necessity for analytical tools to avert the reduction of design to trivial formalism. The study's outcome suggests that understanding and leveraging the traits of latent space can nurture a more meaningful engagement with AI-driven design, presenting a novel approach to architectural creativity.
keywords	Latent Space, Generative Artificial Intelligence, Text-to-image Generation, Architectural Creativity, Spatial Analysis
series	SIGraDi
email
last changed	2024/03/08 14:07

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