Video face re-aging deals with altering the apparent age of a person to the target age in videos. This problem is challenging due to the lack of paired video datasets maintaining temporal consistency in identity and age. Most re-aging methods process each image individually without considering the temporal consistency of videos. While some existing works address the issue of temporal coherence through video facial attribute manipulation in latent space, they often fail to deliver satisfactory performance in age transformation. To tackle the issues, we propose (1) a novel synthetic video dataset that features subjects across a diverse range of age groups; (2) a baseline architecture designed to validate the effectiveness of our proposed dataset, and (3) the development of three novel metrics tailored explicitly for evaluating the temporal consistency of video re-aging techniques. Our comprehensive experiments on public datasets, such as VFHQ and CelebV-HQ, show that our method outperforms the existing approaches in terms of both age transformation and temporal consistency.

翻译：视频人脸重龄旨在改变视频中人物面部呈现的年龄至目标年龄。该问题具有挑战性，主要因为缺乏同时保持身份与年龄时间一致性的配对视频数据集。现有大多数重老化方法独立处理每帧图像，未考虑视频的时间一致性。尽管部分已有工作通过潜在空间中的视频面部属性操作解决了时间连贯性问题，但其在年龄转换中往往未能达到令人满意的性能。为解决上述问题，我们提出：(1) 一个新颖的合成视频数据集，包含跨年龄段的多年龄组对象；(2) 一个用于验证所提数据集有效性的基线架构；(3) 专门针对视频重老化技术时间一致性评估的三项新型度量指标。在VFHQ和CelebV-HQ等公开数据集上的全面实验表明，我们的方法在年龄转换和时间一致性两方面均优于现有方法。