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

Understand

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.