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Conventional aging simulations require the collection of facial images by age group; thus, a lack of training data poses an issue. As a result, generative AI that learns from big data has attracted attention. However, generative AI has the characteristic of generating plausible results but not necessarily accurate and has the drawback of lacking objectivity. This research aims to perform an objective aging simulation while leveraging the diverse facial expression generation capabilities of generative AI trained on big data to compensate for the lack of training data. When compared with aging simulations that utilizes prompt instructions with generative AI, the results tended to be uniform aging simulations for all individuals. However, the proposed method results in an aging simulation that reflects individual characteristics, and it was confirmed that an objective simulation can be realized while taking advantage of generative AI's ability to generate plausible images.

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Facial Image Aging Simulation and Domain Transformation Using Generative AI

  • 会议论文

  • 发表日期：

    2026年

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  • Takeshi Nagata,
  • Yuna Kawamoto,
  • Takumi Enari,
  • Hiroshi Endo,
  • Syuhei Ozawa,
  • Hina Kawasaki,
  • Chie Nyui,
  • Aiko Ishihara,
  • Natsumi Nishiyama,
  • Tamami Sanbongi,
  • Chandra Louis,
  • kiyoshi Hizukuri

摘要

Conventional aging simulations require the collection of facial images by age group; thus, a lack of training data poses an issue. As a result, generative AI that learns from big data has attracted attention. However, generative AI has the characteristic of generating plausible results but not necessarily accurate and has the drawback of lacking objectivity. This research aims to perform an objective aging simulation while leveraging the diverse facial expression generation capabilities of generative AI trained on big data to compensate for the lack of training data. When compared with aging simulations that utilizes prompt instructions with generative AI, the results tended to be uniform aging simulations for all individuals. However, the proposed method results in an aging simulation that reflects individual characteristics, and it was confirmed that an objective simulation can be realized while taking advantage of generative AI's ability to generate plausible images.