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

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arxiv 2302.09466 v3 pith:QATHXODM submitted 2023-02-19 cs.HC cs.AI

classification cs.HCcs.AI
keywords imagestextai-generatedmodelprecisepromptsrepromptautomatic
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Prompt Evolution for Generative AI: A Classifier-Guided Approach

    cs.LG 2023-05 unverdicted novelty 6.0 of 10

    The paper introduces a classifier-guided multi-objective evolutionary algorithm for prompt evolution in generative AI that uses the model's stochastic generation as implicit mutations to create Pareto-optimized images...

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