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_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	caadria2023_1
id	caadria2023_1
authors	Koh, Immanuel
year	2023
title	AI-Bewitched Architecture of Hansel and Gretel: Food-to-Architecture in 2D & 3D with GANs and Diffusion Models
doi	https://doi.org/10.52842/conf.caadria.2023.1.009
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. 9–18
summary	Architects such as Le Corbusier, Frank Gehry, Aldo Rossi, and Greg Lynn have implicitly turned culinary formalism into architectural formalism during their careers. How might AI assist in a similar act of bisociation (or conceptual blending)? The paper is the first to explore this food2architecture bisociation explicitly, and specifically with generative adversarial networks (GANs) such as CycleGAN and VQGAN-CLIP, and diffusion models such as OpenAI’s DALL-E 2, Midjourney and DreamFusion (using Stable Diffusion). Instead of using textual input prompts to generate images of architecture only with the discipline’s own vocabulary, the research merges them with the vocabulary of food, thus exploiting their potential in blending their respective conceptual and formal characteristics. While these diffusion models have recently been used by the general public to generate 2D imagery posts on various social media platforms, no existing work has conducted a detailed and systematic analysis on their exclusive capacity in bisociating food and architecture. Imagery outputs generated during two workshops involving 150 designers and non-designers are included here as illustrations. Beginning and ending the paper with the all-familiar fairy tale of the gingerbread house, the research explores the creative design bisociative affordance of today's text-to-image and text-to-3D models by turning culinary inputs into architectural outputs -- envisioning an explicitly computational version of the implicit 'food2architecture' mental models plausibly used by some of the most creative architects.
keywords	Deep Learning, Midjourney, DALL-E 2, DreamFusion, Stable Diffusion, GANs
series	CAADRIA
email
last changed	2023/06/15 23:14

_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	ijac202321201
id	ijac202321201
authors	Steinfeld, Kyle
year	2023
title	Clever little tricks: A socio-technical history of text-to-image generative models
source	International Journal of Architectural Computing 2023, Vol. 21 - no. 2, 211–241
summary	The emergence of text-to-image generative models (e.g., Midjourney, DALL-E 2, Stable Diffusion) in the summer of 2022 impacted architectural visual culture suddenly, severely, and seemingly out of nowhere. To contextualize this phenomenon, this text offers a socio-technical history of text-to-image generative systems. Three moments in time, or “scenes,” are presented here: the first at the advent of AI in the middle of the last century; the second at the “reawakening” of a specific approach to machine learning at the turn of this century; the third that documents a rapid sequence of innovations, dubbed “clever little tricks,” that occurred across just 18 months. This final scene is the crux, and represents the first formal documentation of the recent history of a specific set of informal innovations. These innovations were produced by non-affiliated researchers and communities of creative contributors, and directly led to the technologies that so compellingly captured the architectural imagination in the summer of 2022. Across these scenes, we examine the technologies, application domains, infrastructures, social contexts, and practices that drive technical research and shape creative practice in this space.
keywords	Machine learning, text-to-image, socio-technical study, generative AI
series	journal
last changed	2024/04/17 14:30

_id	ecaade2023_266
id	ecaade2023_266
authors	Çiçek, Selen, Turhan, Gözde Damla and Özkar, Mine
year	2023
title	Reconsidering Design Pedagogy through Diffusion Models
doi	https://doi.org/10.52842/conf.ecaade.2023.1.031
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. 31–40
summary	The text-to-image based diffusion models are deep learning models that generate images from text-based narratives in user-generated prompts. These models use natural language processing (NLP) techniques to recognize narratives and generate corresponding images. This study associates the assignment-based learning-by-doing of design studio with the prompt-based diffusion models that require fine-tuning in each image generation. The reference is a specific formal education setup developed within the context of compulsory courses in design programs’ curricula. We explore the implications of diffusion models for a model of the basic design studio as a case study. The term basic design implies a core and foundational element of design. To explore and evaluate the potential of AI tools to improve novice designers’ design problem solving capabilities, a retrospective analysis was conducted for a series of basic design studio assignments. The first step of the study was to reframe the assignment briefs as design problems and student design works as design solutions. The outcomes of the identification were further used as input data to generate synthetic design solutions by text-to-image diffusion models. In the third step, the design solution sets generated by students and the diffusion models were comparatively assessed by design experts with regards to how well they answered to the design problems defined in the briefs. The initial findings showed that diffusion models were able to generate a myriad of design solutions in a short time. It is conjectured that this might help students to easily understand the ill-defined design problem requirements and generate visual concepts based on written descriptions. However, the comparison indicated the value of design reasoning conveyed in the studio, as it gets highlighted with the lack of improvement in the learning curve of the diffusion model recorded through the synthetic design process.
keywords	Deep Learning, Diffusion Models, Design Education, Basic Design, Design Problems
series	eCAADe
email
last changed	2023/12/10 10:49

_id	cdrf2023_35
id	cdrf2023_35
authors	Zexi Lyu, Zao Li, Zijing Wu
year	2023
title	Research on Image-to-Image Generation and Optimization Methods Based on Diffusion Model Compared with Traditional Methods: Taking Façade as the Optimization Object
doi	https://doi.org/https://doi.org/10.1007/978-981-99-8405-3_4
source	Proceedings of the 2023 DigitalFUTURES The 5st International Conference on Computational Design and Robotic Fabrication (CDRF 2023)
summary	The intersection of technology and culture has become a topic of great interest worldwide, with China's development embracing this integration as an essential direction. One critical area where these two fields converge is in the inheritance, translation, and creative design of cultural heritage. In line with this trend, our study explores the potential of stable diffusion to produce highly detailed and visually stunning building façades. We start by providing an overall survey and algorithm fundamentals of the generative deep learning models used so far, namely, GAN and Diffusion models. Then, we present our methodology for using Diffusion Model to generate architecture façades. We then demonstrate how the fine-tuning is done for Stable Diffusion is done to yield optimal performance and then evaluate four different training methods of SD. We also compare existing GAN based façade generation method with our Diffusion based method. Our results show that our Diffusion-based approach outperforms existing methods in terms of detail and quality, highlighting the potential of stable diffusion in generating visually appealing building façades. This research contributes to the growing body of work on the integration of technology and culture in architecture and offers insights into the potential of stable diffusion for creative design applications.
series	cdrf
email
last changed	2024/05/29 14:04

_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_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	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

_id	ecaade2023_197
id	ecaade2023_197
authors	Kim, Frederick Chando, Johanes, Mikhael and Huang, Jeffrey
year	2023
title	Text2Form Diffusion: Framework for learning curated architectural vocabulary
doi	https://doi.org/10.52842/conf.ecaade.2023.1.079
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. 79–88
summary	Stepping towards the machine learning age in architecture, many architects and researchers have developed creative ways of utilizing machine learning with domain-specific architectural datasets in recent years. With the rising popularity of large language-based text-to-image models, architects have relied on different strategies for developing the prompt to create satisfactory images representing architecture, which lessens the agency of the architects in the process. We explore alternative ways of working with such models and put forward the role of designers through the fine-tuning process. This research proses a fine-tuning framework of a pre-trained language model, namely Stable Diffusion, with a dataset of formal architectural vocabularies towards developing a new way of understanding architectural form through human-machine collaboration. This paper explores the creation of an annotation system for machines to learn and understand architectural forms. The results showcased a promising method combining different formal characteristics for architectural form generation and ultimately contributing to the discourse of form and language in architecture in the age of large deep learning models.
keywords	machine learning, diffusion model, architectural form, text-to-architecture
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	acadia23_v2_508
id	acadia23_v2_508
authors	Koehler, Daniel; liu, Zidong
year	2023
title	Exploring Building Typologies and their Socioeconomic Contexts: Compositional Insights from Large-Scale-Text-to-Image Models
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 508-517.
summary	This study utilizes large-scale-text-to-image (LLI) models to investigate possibilities to describe building types data-centric. With the introduction of ""data-centric typologies"" we hope to challenge traditional architectural classification systems, while reviving type as an architectural strategy to link socio-economic contexts to the physical form of a place. By examining artificial intelligence (AI)-generated images of various city buildings, the research explores compositional characteristics, realism, and model limitations. We generated and segmented a synthetic dataset of 15,000 images into individual building segments, conducting a statistical analysis of compositional features across 500 cities. Despite dataset biases and limitations, our results indicate that synthetic databases provide a deeper analytical basis than traditional methods. The generated dataset alone paints forensic landscapes of locales that are not typically showcased. Particularly from a pedagogical perspective, data-centric investigations can serve as a valuable tool for illustrating the diversity of cities and living modes. The findings show that socio-economic attributes, like quality of life, are more closely tied to neighborhoods or projects than entire cities. Consequently, architectural typologies are most effective at a human-ori- ented scale, interfacing city with architecture.
series	ACADIA
type	paper
email
last changed	2024/12/20 09:13

_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	ecaade2023_456
id	ecaade2023_456
authors	Riether, Gernot and Narahara, Taro
year	2023
title	AI Tools to Synthesize Characteristics of Public Spaces
doi	https://doi.org/10.52842/conf.ecaade.2023.2.831
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. 831–838
summary	This paper investigates how artificial intelligence (AI) tools can be used in the early design phase of public space to synthesize qualitative information such as cultural differences and individual perception into a common understanding of qualities of a public space. In that way AI tools can be used to synthesize emotional responses citizens may have to urban spaces as a form of feedback during the design process. To do that the investigation leverages AI tools' text-guided image-to-image translation techniques, their capacity to assess the association between images and texts and the premise for synthesizing a common understanding of characteristics and qualities of public spaces.
keywords	Urban Public Space, AI tools, Stable Diffusion
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	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	acadia23_v2_430
id	acadia23_v2_430
authors	Vaidhyanathan, Vishal; T R, Radhakrishnan; Garcia del Castillo Lopez, Jose Luis
year	2023
title	Spacify: A Generative Framework for Spatial Comprehension, Articulation and Visualization using Large Language Models (LLMs) and eXtended Reality (XR)
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 430-443.
summary	Spatial design, the thoughtful planning and creation of built environments, typically requires advanced technical knowledge and visuospatial skills, making it largely exclusive to professionals like architects, interior designers, and urban designers. This exclusivity limits non-experts' access to spatial design, despite their ability to describe requirements and suggestions in natural language. Recent advancements in generative artificial intelligence (AI), particularly large language models (LLMs), and extended reality, (XR) offer the potential to address this limitation. This paper introduces Spacify (Figure 1), a framework that utilizes the generalizing capabilities of LLMs, 3D data-processing, and XR interfaces to create an immersive medium for language-driven spatial understanding, design, and visualization for non-experts. This paper describes the five components of Spacify: External Data, User Input, Spatial Interface, Large Language Model, and Current Spatial Design; which enable the use of generative AI models in a) question/ answering about 3D spaces with reasoning, b) (re)generating 3D spatial designs with natural language prompts, and c) visualizing designed 3D spaces with natural language descriptions. An implementation of Spacify is demonstrated via an XR smartphone application, allowing for an end-to-end, language-driven interior design process. User survey results from non-experts redesigning their spaces in 3D using this application suggest that Spacify can make spatial design accessible using natural language prompts, thereby pioneering a new realm of spatial design that is naturally language-driven.
series	ACADIA
type	paper
email
last changed	2024/12/20 09:12

_id	acadia23_v3_49
id	acadia23_v3_49
authors	A. Noel, Vernelle; Dortdivanlioglu, Hayri
year	2023
title	Text-to-image generators: Semiotics, Semantics, Syntax, and Society
source	ACADIA 2023: Habits of the Anthropocene: Scarcity and Abundance in a Post-Material Economy [Volume 3: Proceedings of the 43rd Annual Conference for the Association for Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9891764-1-0]. Denver. 26-28 October 2023. edited by A. Crawford, N. Diniz, R. Beckett, J. Vanucchi, M. Swackhamer 24-32.
summary	Text-to-image generators, such as Midjourney, DALL-E, and Stable Diffusion, are becoming increasingly popular. These generators, whose models are trained on large datasets of text-image pairs, often scraped from the web, take text prompts as input, and use them to generate images—text-to-image prompting. In this visual essay, we raise questions about the entanglement of semiotics, semantics, syntax, and society in these text-to-image generator tools. We are intrigued by how these technologies are “intrawoven” with social and cultural contexts. How are their constructions and presentations reconfigurations? How do, or might they, inform pedagogy, theory, methods, and our publics? To explore these questions, we entered six prompts related to the built environment in six different languages, eight months apart in Midjourney (“Midjourney” n.d.). The generated images (Figure 1), require that we ask deep questions of each image, in comparison with each other, across each group of four, and across time (eight months apart). We argue that text-to-image generators call for a rigorous exploration of semiotics, semantics, syntax, and the society, with implications for pedagogy, theory-building, methodologies, and public enlightenment. Furthermore, we assert that these tools can facilitate pertinent questions about the relationships between technology and society. This is just the beginning. For now, we have questions.
series	ACADIA
type	field note
email
last changed	2024/04/17 13:59

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