Video Face Re-Aging: Toward Temporally Consistent Face Re-Aging

TL;DR

This paper introduces a new synthetic video dataset, a baseline architecture, and novel metrics for evaluating temporally consistent face re-aging in videos, demonstrating improved performance over existing methods.

Contribution

The work presents a synthetic video dataset, a baseline model, and new evaluation metrics specifically designed for temporally consistent face re-aging.

Findings

Outperforms existing methods in age transformation accuracy.

Achieves higher temporal consistency in video re-aging.

User studies favor the proposed method for temporal coherence.

Abstract

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.