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：视频人脸重老化旨在将视频中人物的年龄变换为目标年龄。该问题的挑战在于缺乏在身份和年龄上保持时间一致性的配对视频数据集。大多数重老化方法逐帧处理图像，未考虑视频的时间一致性。尽管现有一些工作通过潜在空间中的视频面部属性操控处理时间连贯性问题，但在年龄变换任务中往往表现不佳。为解决这些问题，我们提出：（1）一个涵盖不同年龄组对象的新颖合成视频数据集；（2）用于验证所提数据集有效性的基准架构；（3）针对视频重老化技术时间一致性评估专门设计的三种新型指标。我们在VFHQ和CelebV-HQ等公共数据集上的综合实验表明，我们的方法在年龄变换和时间一致性方面均优于现有方法。