  Text-to-image (T2I) diffusion models achieve state-of-the-art results in
image synthesis and editing. However, leveraging such pretrained models for
video editing is considered a major challenge. Many existing works attempt to
enforce temporal consistency in the edited video through explicit
correspondence mechanisms, either in pixel space or between deep features.
These methods, however, struggle with strong nonrigid motion. In this paper, we
introduce a fundamentally different approach, which is based on the observation
that spatiotemporal slices of natural videos exhibit similar characteristics to
natural images. Thus, the same T2I diffusion model that is normally used only
as a prior on video frames, can also serve as a strong prior for enhancing
temporal consistency by applying it on spatiotemporal slices. Based on this
observation, we present Slicedit, a method for text-based video editing that
utilizes a pretrained T2I diffusion model to process both spatial and
spatiotemporal slices. Our method generates videos that retain the structure
and motion of the original video while adhering to the target text. Through
extensive experiments, we demonstrate Slicedit's ability to edit a wide range
of real-world videos, confirming its clear advantages compared to existing
competing methods. Webpage: https://matankleiner.github.io/slicedit/
