Empowering Clients: Transformation of Design Processes Due to Generative AI

• URL: http://arxiv.org/abs/2411.15061v1
• Date: Fri, 22 Nov 2024 16:48:15 GMT
• Title: Empowering Clients: Transformation of Design Processes Due to Generative AI
• Authors: Johannes Schneider, Kilic Sinem, Daniel Stockhammer,
• Abstract summary: The study reveals that AI can disrupt the ideation phase by enabling clients to engage in the design process through rapid visualization of their own ideas. Our study shows that while AI can provide valuable feedback on designs, it might fail to generate such designs, allowing for interesting connections to foundations in computer science. Our study also reveals that there is uncertainty among architects about the interpretative sovereignty of architecture and loss of meaning and identity when AI increasingly takes over authorship in the design process.
• Score: 1.4003044924094596
• License:
• Abstract: The domain of computational design, driven by advancements in Generative AI, is transforming creative fields. We explore the transformative effects of Generative AI on the architectural design process and discuss the role of the architect. The case of architecture is interesting as designing houses is complex, involving extensive customer interaction. We employ a within-subject experiment using a popular general-purpose text-to-image tool for generating designs and providing feedback on existing designs, followed by expert interviews. The study reveals that AI can disrupt the ideation phase by enabling clients to engage in the design process through rapid visualization of their own ideas. In turn, the architect's role shifts more towards assessing the feasibility of designs generated conjointly by clients and AI. Our study also shows that while AI can provide valuable feedback on designs, it might fail to generate such designs, allowing for interesting connections to foundations in computer science, i.e., NP-completeness. AI's feedback also tends to hamper creativity and innovation by suggesting altering novel, innovative approaches toward more standardized designs. Our study also reveals that there is uncertainty among architects about the interpretative sovereignty of architecture and loss of meaning and identity when AI increasingly takes over authorship in the design process.

Related papers

• Diffusion-Based Visual Art Creation: A Survey and New Perspectives [51.522935314070416]
  This survey explores the emerging realm of diffusion-based visual art creation, examining its development from both artistic and technical perspectives. Our findings reveal how artistic requirements are transformed into technical challenges and highlight the design and application of diffusion-based methods within visual art creation. We aim to shed light on the mechanisms through which AI systems emulate and possibly, enhance human capacities in artistic perception and creativity.
  arXiv  Detail & Related papers  (2024-08-22T04:49:50Z)
• Inspired by AI? A Novel Generative AI System To Assist Conceptual Automotive Design [6.001793288867721]
  Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to gather image ideas, followed by sketching on paper or using digital tools to develop concepts. Emerging generative AI techniques, such as diffusion models, offer a promising avenue to streamline these processes by swiftly generating design concepts based on text and image inspiration inputs.
  arXiv  Detail & Related papers  (2024-06-06T17:04:14Z)
• I-Design: Personalized LLM Interior Designer [57.00412237555167]
  I-Design is a personalized interior designer that allows users to generate and visualize their design goals through natural language communication. I-Design starts with a team of large language model agents that engage in dialogues and logical reasoning with one another. The final design is then constructed in 3D by retrieving and integrating assets from an existing object database.
  arXiv  Detail & Related papers  (2024-04-03T16:17:53Z)
• Sketch-to-Architecture: Generative AI-aided Architectural Design [20.42779592734634]
  We present a novel workflow that utilizes AI models to generate conceptual floorplans and 3D models from simple sketches. Our work demonstrates the potential of generative AI in the architectural design process, pointing towards a new direction of computer-aided architectural design.
  arXiv  Detail & Related papers  (2024-03-29T14:04:45Z)
• Geometric Deep Learning for Computer-Aided Design: A Survey [85.79012726689511]
  This survey offers a comprehensive overview of learning-based methods in computer-aided design. It includes similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds. It provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain.
  arXiv  Detail & Related papers  (2024-02-27T17:11:35Z)
• Zero-shot Sequential Neuro-symbolic Reasoning for Automatically Generating Architecture Schematic Designs [4.78070970632469]
  This paper introduces a novel automated system for generating architecture schematic designs. We leverage the combined strengths of generative AI (neuro reasoning) and mathematical program solvers (symbolic reasoning) Our method can generate various building designs in accordance with the understanding of the neighborhood.
  arXiv  Detail & Related papers  (2024-01-25T12:52:42Z)
• Grasping AI: experiential exercises for designers [8.95562850825636]
  We investigate techniques for exploring and reflecting on the interactional affordances, the unique relational possibilities, and the wider social implications of AI systems. We find that exercises around metaphors and enactments make questions of training and learning, privacy and consent, autonomy and agency more tangible.
  arXiv  Detail & Related papers  (2023-10-02T15:34:08Z)
• Unfinished Architectures: A Perspective from Artificial Intelligence [73.52315464582637]
  Development of Artificial Intelligence (AI) opens new avenues for the proposal of possibilities for the completion of unfinished architectures. Recent appearance of tools such as DALL-E, capable of completing images guided by a textual description. In this article we explore the use of these new AI tools for the completion of unfinished facades of historical temples and analyse the still germinal stadium in the field of architectural graphic composition.
  arXiv  Detail & Related papers  (2023-03-03T13:05:10Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques [55.492211642128446]
  Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. Model-based computational design algorithms assist designers by generating design examples during design. Black box methods for assistance, on the other hand, can work with any design problem.
  arXiv  Detail & Related papers  (2022-04-15T20:40:43Z)
• Towards A Process Model for Co-Creating AI Experiences [16.767362787750418]
  Thinking of technology as a design material is appealing to designers. As a material, AI resists this approach because its properties emerge as part of the design process itself. We investigate the co-creation process through a design study with 10 pairs of designers and engineers.
  arXiv  Detail & Related papers  (2021-04-15T16:53:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.