An important milestone for AI is the development of algorithms that can produce drawings that are indistinguishable from those of humans. Here, we adapt the 'diversity vs. recognizability' scoring framework from Boutin et al, 2022 and find that one-shot diffusion models have indeed started to close the gap between humans and machines. However, using a finer-grained measure of the originality of individual samples, we show that strengthening the guidance of diffusion models helps improve the humanness of their drawings, but they still fall short of approximating the originality and recognizability of human drawings. Comparing human category diagnostic features, collected through an online psychophysics experiment, against those derived from diffusion models reveals that humans rely on fewer and more localized features. Overall, our study suggests that diffusion models have significantly helped improve the quality of machine-generated drawings; however, a gap between humans and machines remains -- in part explainable by discrepancies in visual strategies.

翻译：一个重要的AI里程碑是开发能够生成与人类画作难以区分的绘图算法。本文采用Boutin等人（2022）提出的"多样性vs.可识别性"评分框架，发现单次扩散模型确实已开始缩小人与机器之间的差距。然而，通过更精细的个体样本原创性度量，我们证明强化扩散模型的引导有助于提升其画作的类人性，但在近似人类画作的原创性和可识别性方面仍存在不足。通过在线心理物理学实验收集的人类类别诊断特征与扩散模型提取的特征进行比较，发现人类依赖于更少且更局部的特征。总体而言，我们的研究表明，扩散模型显著提升了机器生成绘图的质量；但人与机器之间仍存在差距——部分原因可归因于视觉策略的差异。