Video Face Re-Aging: Toward Temporally Consistent Face Re-Aging
This addresses the challenge of video face re-aging for applications like film editing or digital content creation, but it is incremental as it builds on existing video manipulation techniques.
The paper tackled the problem of altering a person's apparent age in videos while maintaining temporal consistency, and their method outperformed existing approaches in age transformation accuracy and temporal consistency, with user studies showing preferences of 48.1% for older and 39.3% for younger directions.
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.