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 novel metrics tailored explicitly for evaluating the temporal consistency of video re-aging techniques. Our comprehensive experiments on public datasets, including VFHQ and CelebA-HQ, show that our method outperforms existing approaches in age transformation accuracy and temporal consistency. Notably, in user studies, our method was preferred for temporal consistency by 48.1\% of participants for the older direction and by 39.3\% for the younger direction.

翻译：视频人脸年龄重制旨在改变视频中人物的面部年龄至目标年龄。由于缺乏在身份和年龄维度上保持时序一致性的配对视频数据集，该问题颇具挑战性。多数年龄重制方法逐帧独立处理图像，未考虑视频的时间一致性。尽管现有部分工作通过潜在空间中的视频面部属性操作解决了时序连贯性问题，但在年龄变换任务中往往表现欠佳。为解决上述问题，我们提出：（1）一个涵盖各年龄段人物的新型合成视频数据集；（2）用于验证所提数据集有效性的基线架构；（3）专门针对视频年龄重制技术时序一致性评估的新型度量标准。在VFHQ和CelebA-HQ等公开数据集上的综合实验表明，本方法在年龄变换精度和时序一致性方面均优于现有方案。值得注意的是，在用户研究中，本方法在时序一致性方面获得48.1%参与者偏好（老化方向）和39.3%参与者偏好（年轻化方向）。