  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.
