文本到图像生成模型能否准确描绘年龄？关于合成肖像生成与年龄估计的比较研究
Can Text-to-Image Generative Models Accurately Depict Age? A Comparative Study on Synthetic Portrait Generation and Age Estimation
AI总结：
本文系统评估了文本到图像模型在合成肖像中描绘年龄、国籍和性别的准确性，发现其虽能生成多样身份面孔，但对特定年龄的刻画精度高度不稳定，难以满足高精度年龄相关任务需求。
摘要：
文本到图像生成模型在生成多样且逼真的输出方面已取得显著进展。本文对其在创建能够准确表征各种人口统计学属性的合成肖像方面的有效性进行了全面分析，特别关注年龄、国籍和性别。我们的评估采用指定详细画像的提示（例如，“一张32岁加拿大男性的逼真自拍照”），覆盖212个国籍、从10岁到78岁的30个不同年龄以及均衡的性别代表性。我们将生成的图像与两个成熟的年龄估计模型给出的真实年龄估计值进行比较，以评估年龄被描绘的忠实程度。我们的发现表明，尽管文本到图像模型能够一致地生成反映不同身份的面孔，但它们在捕捉特定年龄以及在不同人口统计学背景下做到这一点的准确性仍然高度不稳定。这些结果表明，当前的合成数据对于需要稳健精度的高风险年龄相关任务可能不够可靠，除非实践者准备投入大量的筛选和管理工作。尽管如此，在绝对年龄精度并非关键的较不敏感或探索性应用中，它们可能仍然有用。
英文摘要：
Text-to-image generative models have shown remarkable progress in producing diverse and photorealistic outputs. In this paper, we present a comprehensive analysis of their effectiveness in creating synthetic portraits that accurately represent various demographic attributes, with a special focus on age, nationality, and gender. Our evaluation employs prompts specifying detailed profiles (e.g., Photorealistic selfie photo of a 32-year-old Canadian male), covering a broad spectrum of 212 nationalities, 30 distinct ages from 10 to 78, and balanced gender representation. We compare the generated images against ground truth age estimates from two established age estimation models to assess how faithfully age is depicted. Our findings reveal that although text-to-image models can consistently generate faces reflecting different identities, the accuracy with which they capture specific ages and do so across diverse demographic backgrounds remains highly variable. These results suggest that current synthetic data may be insufficiently reliable for high-stakes age-related tasks requiring robust precision, unless practitioners are prepared to invest in significant filtering and curation. Nevertheless, they may still be useful in less sensitive or exploratory applications, where absolute age precision is not critical.
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。
