Cited by 120Digital Media and Visual ArtGenerative Adversarial Networks and Image SynthesisAesthetic Perception and Analysis

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RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions

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Wang, Yunlong, Shen, Shuyuan, Lim, Brian Y·2023·Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

DOI 10.1145/3544548.3581402

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Abstract

Generative AI models have shown impressive ability to produce images with text prompts, which could benefit creativity in visual art creation and self-expression. However, it is unclear how precisely the generated images express contexts and emotions from the input texts. We explored the emotional expressiveness of AI-generated images and developed RePrompt, an automatic method to refine text prompts toward precise expression of the generated images. Inspired by crowdsourced editing strategies, we curated intuitive text features, such as the number and concreteness of nouns, and trained a proxy model to analyze the feature effects on the AI-generated image. With model explanations of the proxy model, we curated a rubric to adjust text prompts to optimize image generation for precise emotion expression. We conducted simulation and user studies, which showed that RePrompt significantly improves the emotional expressiveness of AI-generated images, especially for negative emotions.

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Authors: Wang, Y., Shen, S., & Lim, B. Y.
Affiliations: Agency for Science, Technology and Research (SG)
National University of Singapore (SG)
ORCID: Wang, Yunlong
Shen, Shuyuan
Lim, Brian Y
Venue: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Publisher: ACM
Published: 2023-04-19
Pages: 1-29
DOI: 10.1145/3544548.3581402
OpenAlex: W4321472415
Cited by: 120
References: 86
Citations by year: 2026: 18 · 2025: 48 · 2024: 46 · 2023: 8
FWCI: 35.10
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: CHI Conference on Human Factors in Computing Systems (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: Ministry of Education, India
Institute for Health Innovation and Technology, National University of Singapore
Topics: Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Digital Media and Visual Art
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Life Sciences › Neuroscience › Cognitive Neuroscience › Aesthetic Perception and Analysis
Keywords: AI-generated images, emotional expressiveness, text-to-image generation, prompt refinement, negative emotions, proxy models, model explanation, rubrics, visual art creation

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RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions

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