research-article

C2Ideas: Supporting Creative Interior Color Design Ideation with a Large Language Model

Authors:

Wei ZengAuthors Info & Claims

CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

Article No.: 172, Pages 1 - 18

https://doi.org/10.1145/3613904.3642224

Published: 11 May 2024 Publication History

Abstract

Interior color design is a creative process that endeavors to allocate colors to furniture and other elements within an interior space. While much research focuses on generating realistic interior designs, these automated approaches often misalign with user intention and disregard design rationales. Informed by a need-finding preliminary study, we develop C2Ideas, an innovative system for designers to creatively ideate color schemes enabled by an intent-aligned and domain-oriented large language model. C2Ideas integrates a three-stage process: Idea Prompting stage distills user intentions into color linguistic prompts; Word-Color Association stage transforms the prompts into semantically and stylistically coherent color schemes; and Interior Coloring stage assigns colors to interior elements complying with design principles. We also develop an interactive interface that enables flexible user refinement and interpretable reasoning. C2Ideas has undergone a series of indoor cases and user studies, demonstrating its effectiveness and high recognition of interactive functionality by designers.

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Gao LLu JShao ZLin ZYue SLeong CSun YZauner RWei ZChen S(2024)Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in EducationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345614531:1(514-524)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456145
Show More Cited By

Index Terms

C2Ideas: Supporting Creative Interior Color Design Ideation with a Large Language Model
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI theory, concepts and models
    2. Interactive systems and tools
      1. User interface toolkits
  2. Interaction design
    1. Interaction design process and methods
    2. Interaction design theory, concepts and paradigms

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

May 2024

18961 pages

ISBN:9798400703300

DOI:10.1145/3613904

Editors:
Florian Floyd Mueller
Monash University
,
Penny Kyburz
The Australian National University
,
Julie R. Williamson
University of Glasgow
,
Corina Sas
Lancaster University
,
Max L. Wilson
University of Nottingham
,
Phoebe Toups Dugas
Monash University/New Mexico State University
,
Irina Shklovski
University of Copenhagen

Copyright © 2024 ACM.

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Publication History

Published: 11 May 2024

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Guangzhou Basic and Applied Basic Research Foundation

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CHI '24

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CHI '24: CHI Conference on Human Factors in Computing Systems

May 11 - 16, 2024

HI, Honolulu, USA

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Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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CHI 2025

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ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

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Citations

Cited By

He B(2024)Dynamic Color Design Model Construction Considering Ambient Light ChangesApplied Mathematics and Nonlinear Sciences10.2478/amns-2024-30789:1Online publication date: 7-Nov-2024
https://doi.org/10.2478/amns-2024-3078
Liu ZXie XHe MZhao WWu YCheng LZhang HWu Y(2024)Smartboard: Visual Exploration of Team Tactics with LLM AgentIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345620031:1(23-33)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456200
Gao LLu JShao ZLin ZYue SLeong CSun YZauner RWei ZChen S(2024)Fine-Tuned Large Language Model for Visualization System: A Study on Self-Regulated Learning in EducationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.345614531:1(514-524)Online publication date: 10-Sep-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3456145
Tang TWu YGao JRuan KZhang YYe SWu YChen X(2024)ArtEyer: Enriching GPT-based agents with contextual data visualizations for fine art authenticationVisual Informatics10.1016/j.visinf.2024.11.0018:4(48-59)Online publication date: Dec-2024
https://doi.org/10.1016/j.visinf.2024.11.001

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