对比人类与AI生成图像的创造力，发现人类仍占优势。
Stable diffusion models reveal a persisting human and AI gap in visual creativity
- 用艺术家、普通人和不同提示方式的AI生成图像
- 艺术家创作最富创意，自引导AI最弱，高提示能显著提升AI表现
- 人类与AI对创意判断差异大，凸显视觉创意的人类独特性
尽管近期研究显示大型语言模型在发散思维任务中可媲美人类创造力，但视觉创造力仍缺乏深入探讨。本研究比较了视觉艺术家、非艺术家与图像生成AI模型（两种提示条件：高人类输入的‘人类启发’与低人类输入的‘自我引导’）在图像生成中的表现。由255名人类评分者及GPT4o评估生成图像的创意水平。结果显示清晰的创意梯度：艺术家 > 非艺术家 > 人类启发型AI > 自我引导型AI。增加人类指导显著提升了生成式AI的创意产出，使其接近非艺术家水平。值得注意的是，人类与AI评分者在创意判断上存在显著差异。结果表明，在与语言任务不同，视觉领域中创造力依赖于感知细微差别与情境敏感性，这些是当前生成式AI难以复制的人类能力。
原文摘要 · Abstract (English)
While recent research suggests Large Language Models match human creative performance in divergent thinking tasks, visual creativity remains underexplored. This study compared image generation in human participants (Visual Artists and Non Artists) and using an image generation AI model (two prompting conditions with varying human input: high for Human Inspired, low for Self Guided). Human raters (N=255) and GPT4o evaluated the creativity of the resulting images. We found a clear creativity gradient, with Visual Artists being the most creative, followed by Non Artists, then Human Inspired generative AI, and finally Self Guided generative AI. Increased human guidance strongly improved GenAI's creative output, bringing its productions close to those of Non Artists. Notably, human and AI raters also showed vastly different creativity judgment patterns. These results suggest that, in contrast to language centered tasks, GenAI models may face unique challenges in visual domains, where creativity depends on perceptual nuance and contextual sensitivity, distinctly human capacities that may not be readily transferable from language models.
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