  The burgeoning field of text-based video generation (T2V) has reignited
significant interest in the research of controllable video editing. Although
pre-trained T2V-based editing models have achieved efficient editing
capabilities, current works are still plagued by two major challenges. Firstly,
the inherent limitations of T2V models lead to content inconsistencies and
motion discontinuities between frames. Secondly, the notorious issue of
over-editing significantly disrupts areas that are intended to remain
unaltered. To address these challenges, our work aims to explore a robust
video-based editing paradigm based on score distillation. Specifically, we
propose an Adaptive Sliding Score Distillation strategy, which not only
enhances the stability of T2V supervision but also incorporates both global and
local video guidance to mitigate the impact of generation errors. Additionally,
we modify the self-attention layers during the editing process to further
preserve the key features of the original video. Extensive experiments
demonstrate that these strategies enable us to effectively address the
aforementioned challenges, achieving superior editing performance compared to
existing state-of-the-art methods.
