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Inspired by the impressive performance of recent face image editing methods,several studies have been naturally proposed to extend these methods to theface video editing task. One of the main challenges here is temporalconsistency among edited frames, which is still unresolved. To this end, wepropose a novel face video editing framework based on diffusion autoencodersthat can successfully extract the decomposed features - for the first time as aface video editing model - of identity and motion from a given video. Thismodeling allows us to edit the video by simply manipulating the temporallyinvariant feature to the desired direction for the consistency. Another uniquestrength of our model is that, since our model is based on diffusion models, itcan satisfy both reconstruction and edit capabilities at the same time, and isrobust to corner cases in wild face videos (e.g. occluded faces) unlike theexisting GAN-based methods.

Diffusion Video Autoencoders: Toward Temporally Consistent Face Video Editing via Disentangled Video Encoding