DesignGPT: Multi-Agent Collaboration in Design
- URL: http://arxiv.org/abs/2311.11591v1
- Date: Mon, 20 Nov 2023 08:05:52 GMT
- Title: DesignGPT: Multi-Agent Collaboration in Design
- Authors: Shiying Ding, Xinyi Chen, Yan Fang, Wenrui Liu, Yiwu Qiu, Chunlei Chai
- Abstract summary: DesignGPT uses artificial intelligence agents to simulate the roles of different positions in the design company and allows human designers to collaborate with them in natural language.
Experimental results show that compared with separate AI tools, DesignGPT improves the performance of designers.
- Score: 4.6272626111555955
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Generative AI faces many challenges when entering the product design
workflow, such as interface usability and interaction patterns. Therefore,
based on design thinking and design process, we developed the DesignGPT
multi-agent collaboration framework, which uses artificial intelligence agents
to simulate the roles of different positions in the design company and allows
human designers to collaborate with them in natural language. Experimental
results show that compared with separate AI tools, DesignGPT improves the
performance of designers, highlighting the potential of applying multi-agent
systems that integrate design domain knowledge to product scheme design.
Related papers
- Empowering Clients: Transformation of Design Processes Due to Generative AI [1.4003044924094596]
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.
arXiv Detail & Related papers (2024-11-22T16:48:15Z) - MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis [65.78359025027457]
MetaDesigner revolutionizes artistic typography by leveraging the strengths of Large Language Models (LLMs) to drive a design paradigm centered around user engagement.
A comprehensive feedback mechanism harnesses insights from multimodal models and user evaluations to refine and enhance the design process iteratively.
Empirical validations highlight MetaDesigner's capability to effectively serve diverse WordArt applications, consistently producing aesthetically appealing and context-sensitive results.
arXiv Detail & Related papers (2024-06-28T11:58:26Z) - 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) - Content-Centric Prototyping of Generative AI Applications: Emerging
Approaches and Challenges in Collaborative Software Teams [2.369736515233951]
Our work aims to understand how collaborative software teams set up and apply design guidelines and values, iteratively prototype prompts, and evaluate prompts to achieve desired outcomes.
Our findings reveal a content-centric prototyping approach in which teams begin with the content they want to generate, then identify specific attributes, constraints, and values, and explore methods to give users the ability to influence and interact with those attributes.
arXiv Detail & Related papers (2024-02-27T17:56:10Z) - 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) - Compositional Generative Inverse Design [69.22782875567547]
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem.
We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples.
In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
arXiv Detail & Related papers (2024-01-24T01:33:39Z) - Experiments on Generative AI-Powered Parametric Modeling and BIM for
Architectural Design [4.710049212041078]
The study experiments with the potential of ChatGPT and generative AI in 3D architectural design.
The framework provides architects with an intuitive and powerful method to convey design intent.
arXiv Detail & Related papers (2023-08-01T01:51:59Z) - Exploring Challenges and Opportunities to Support Designers in Learning
to Co-create with AI-based Manufacturing Design Tools [31.685493295306387]
AI-based design tools are proliferating in professional software to assist engineering and industrial designers in complex manufacturing and design tasks.
These tools take on more agentic roles than traditional computer-aided design tools and are often portrayed as "co-creators"
To date, we know little about how engineering designers learn to work with AI-based design tools.
arXiv Detail & Related papers (2023-03-01T02:57:05Z) - 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) - iPLAN: Interactive and Procedural Layout Planning [13.172253981084403]
We propose a new human-in-the-loop generative model, iPLAN.
It is capable of automatically generating layouts, but also interacting with designers throughout the whole procedure.
The results show that iPLAN has high fidelity in producing similar layouts to those from human designers.
arXiv Detail & Related papers (2022-03-27T23:21:15Z)
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.