Inspired by the impressive performance of recent face image editing methods, several studies have been naturally proposed to extend these methods to the face video editing task. One of the main challenges here is temporal consistency among edited frames, which is still unresolved. To this end, we propose a novel face video editing framework based on diffusion autoencoders that can successfully extract the decomposed features - for the first time as a face video editing model - of identity and motion from a given video. This modeling allows us to edit the video by simply manipulating the temporally invariant feature to the desired direction for the consistency. Another unique strength of our model is that, since our model is based on diffusion models, it can satisfy both reconstruction and edit capabilities at the same time, and is robust to corner cases in wild face videos (e.g. occluded faces) unlike the existing GAN-based methods.

翻译：受近期人脸图像编辑方法显著性能的启发，多项研究自然地将这些方法扩展至人脸视频编辑任务。其中关键挑战在于编辑帧之间的时序一致性，这一问题至今尚未解决。为此，我们提出一种基于扩散自编码器的新型人脸视频编辑框架——首次作为人脸视频编辑模型——成功从给定视频中提取解耦的身份特征与运动特征。这种建模方式允许我们通过简单地将时域不变特征向目标方向调整以实现编辑一致性。本模型的另一独特优势在于，由于基于扩散模型结构，它能够同时满足重建与编辑能力，并且相较于现有基于GAN的方法，对野外人脸视频中的边界情况（如遮挡人脸）具有更强的鲁棒性。