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:人工智能生成的模型展示出以文字提示制作图像的惊人能力,这有利于视觉艺术创作和自我表达的创造力;然而,尚不清楚所生成的图像如何准确地表达输入文本中的背景和情感;我们探讨了人工智能生成的图像的情感表达性,并开发了Reprompt,这是改进文本向所生成图像准确表达的自动方法;在众源编辑策略的启发下,我们整理了直观的文字特征,如名词的数量和具体性,并训练了一种代理模型,以分析人工智能生成图像的特征效应;在对代理模型的示范解释下,我们调整了一种符号,以调整文本的提示力,以优化图像生成,实现准确的情感表达;我们进行了模拟和用户研究,结果表明,人工智能生成图像的情感表达性显著改善,特别是负面情绪的情感。