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.

翻译：摘要：生成式AI模型展现出通过文本提示生成图像的惊人能力，这有益于视觉艺术创作与自我表达中的创造力发挥。然而，生成图像究竟能在多大程度上精确表达输入文本的语境与情感仍不明确。我们探究了AI生成图像的情感表达能力，并开发了RePrompt这一自动方法，通过优化文本提示使生成图像实现精准表达。受众包编辑策略启发，我们提炼出直观的文本特征（如名词数量及其具体性），并训练代理模型分析这些特征对AI生成图像的影响。基于代理模型的解释，我们构建了一套评估准则以调整文本提示，从而优化图像生成以实现精确的情感表达。通过仿真实验与用户研究，结果表明RePrompt能显著提升AI生成图像的情感表达能力，尤其对负面情绪的呈现效果尤为突出。