A Video is Worth 256 Bases: Spatial-Temporal Expectation-Maximization
  Inversion for Zero-Shot Video Editing
Maomao Li Yu Li Tianyu Yang Yunfei Liu Dongxu Yue Zhihui Lin Dong Xu
This paper presents a video inversion approach for zero-shot video editing,
which aims to model the input video with low-rank representation during the
inversion process. The existing video editing methods usually apply the typical
2D DDIM inversion or na\"ive spatial-temporal DDIM inversion before editing,
which leverages time-varying representation for each frame to derive noisy
latent. Unlike most existing approaches, we propose a Spatial-Temporal
Expectation-Maximization (STEM) inversion, which formulates the dense video
feature under an expectation-maximization manner and iteratively estimates a
more compact basis set to represent the whole video. Each frame applies the
fixed and global representation for inversion, which is more friendly for
temporal consistency during reconstruction and editing. Extensive qualitative
and quantitative experiments demonstrate that our STEM inversion can achieve
consistent improvement on two state-of-the-art video editing methods.