测试生成人脸年龄准确性，发现模型表现参差不齐。
Can Text-to-Image Generative Models Accurately Depict Age? A Comparative Study on Synthetic Portrait Generation and Age Estimation
- 用详细提示词生成212国、30个年龄的人像
- 与两个年龄估计算法对比，年龄刻画精度不一
- 适合探索性应用，高精度任务需额外筛选
文本到图像生成模型在生成多样且逼真的图像方面取得了显著进展。本文系统评估了其在生成能准确反映人口统计特征（重点关注年龄、国籍和性别）的合成肖像方面的效果。实验使用包含详细身份信息的提示词（如：32岁加拿大男性的写实自拍），覆盖212种国籍、30个年龄（10至78岁）及均衡性别分布。通过与两个成熟的年龄估计算法的真值结果对比，评估生成图像对年龄的刻画准确性。结果显示，尽管模型能稳定生成反映不同身份的面部，但在捕捉具体年龄及其跨人群一致性方面表现差异显著。这表明当前合成数据在需要高精度的年龄相关任务中可靠性不足，除非进行大量过滤与人工校验。但其仍可用于对年龄精度要求不高的探索性或非敏感场景。
原文摘要 · Abstract (English)
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.
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