VIA: A Spatiotemporal Video Adaptation Framework for Global and Local
  Video Editing
Authors: name: Jing Gu, name: Yuwei Fang, name: Ivan Skorokhodov, name: Peter Wonka, name: Xinya Du, name: Sergey Tulyakov, name: Xin Eric Wang

Video editing stands as a cornerstone of digital media, from entertainment
and education to professional communication. However, previous methods often
overlook the necessity of comprehensively understanding both global and local
contexts, leading to inaccurate and inconsistency edits in the spatiotemporal
dimension, especially for long videos. In this paper, we introduce VIA, a
unified spatiotemporal VIdeo Adaptation framework for global and local video
editing, pushing the limits of consistently editing minute-long videos. First,
to ensure local consistency within individual frames, the foundation of VIA is
a novel test-time editing adaptation method, which adapts a pre-trained image
editing model for improving consistency between potential editing directions
and the text instruction, and adapts masked latent variables for precise local
control. Furthermore, to maintain global consistency over the video sequence,
we introduce spatiotemporal adaptation that adapts consistent attention
variables in key frames and strategically applies them across the whole
sequence to realize the editing effects. Extensive experiments demonstrate
that, compared to baseline methods, our VIA approach produces edits that are
more faithful to the source videos, more coherent in the spatiotemporal
context, and more precise in local control. More importantly, we show that VIA
can achieve consistent long video editing in minutes, unlocking the potentials
for advanced video editing tasks over long video sequences.