  In the dynamic field of digital content creation using generative models,
state-of-the-art video editing models still do not offer the level of quality
and control that users desire. Previous works on video editing either extended
from image-based generative models in a zero-shot manner or necessitated
extensive fine-tuning, which can hinder the production of fluid video edits.
Furthermore, these methods frequently rely on textual input as the editing
guidance, leading to ambiguities and limiting the types of edits they can
perform. Recognizing these challenges, we introduce AnyV2V, a novel tuning-free
paradigm designed to simplify video editing into two primary steps: (1)
employing an off-the-shelf image editing model to modify the first frame, (2)
utilizing an existing image-to-video generation model to generate the edited
video through temporal feature injection. AnyV2V can leverage any existing
image editing tools to support an extensive array of video editing tasks,
including prompt-based editing, reference-based style transfer, subject-driven
editing, and identity manipulation, which were unattainable by previous
methods. AnyV2V can also support any video length. Our evaluation indicates
that AnyV2V significantly outperforms other baseline methods in automatic and
human evaluations by significant margin, maintaining visual consistency with
the source video while achieving high-quality edits across all the editing
tasks.
