从铅笔到像素：儿童、成人与AI跨群体创意绘画的系统性研究
Pencils to Pixels: A Systematic Study of Creative Drawings across Children, Adults and AI
AI总结：
针对不同智能体绘画视觉创造力量化问题，构建含儿童、成人及AI共1338幅作品的数据集，从风格与内容两方面分析差异，提出首个跨模态研究框架并发现专家与自动化评分的不一致。
摘要：
我们能否推导出计算指标，以量化不同智能体绘画中的视觉创造力，同时考虑技术技能和风格的固有差异？为解答此问题，我们构建了一个包含1338幅绘画的新数据集，涵盖儿童、成人和AI在创意绘画任务中的作品。我们从两个方面对绘画进行表征：（1）风格和（2）内容。对于风格，我们定义了墨水密度、墨水分布和元素数量的度量。对于内容，我们使用专家标注的类别来研究概念多样性，并使用图像和文本嵌入来计算距离度量。我们比较了儿童、成人和AI绘画的风格、内容和创造力，并构建了简单模型来预测专家和自动化的创造力评分。我们发现这些群体在风格和内容上存在显著差异——儿童的绘画包含更多元素，AI的绘画具有更高的墨水密度，而成人的绘画展现出最大的概念多样性。值得注意的是，我们强调了通过专家和自动化评分获得的创造力判断之间的不一致，并讨论了其影响。据我们所知，通过这些努力，我们的工作首次提供了超越文本模态研究人类和人工智能创造力的框架，并试图得出创造力背后的领域无关原则。我们的数据和脚本可在GitHub上获取。
英文摘要：
Can we derive computational metrics to quantify visual creativity in drawings across intelligent agents, while accounting for inherent differences in technical skill and style? To answer this, we curate a novel dataset consisting of 1338 drawings by children, adults and AI on a creative drawing task. We characterize two aspects of the drawings -- (1) style and (2) content. For style, we define measures of ink density, ink distribution and number of elements. For content, we use expert-annotated categories to study conceptual diversity, and image and text embeddings to compute distance measures. We compare the style, content and creativity of children, adults and AI drawings and build simple models to predict expert and automated creativity scores. We find significant differences in style and content in the groups -- children's drawings had more components, AI drawings had greater ink density, and adult drawings revealed maximum conceptual diversity. Notably, we highlight a misalignment between creativity judgments obtained through expert and automated ratings and discuss its implications. Through these efforts, our work provides, to the best of our knowledge, the first framework for studying human and artificial creativity beyond the textual modality, and attempts to arrive at the domain-agnostic principles underlying creativity. Our data and scripts are available on GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
