  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.
