AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks
Authors: name: Max Ku, name: Cong Wei, name: Weiming Ren, name: Huan Yang, name: Wenhu Chen

Video-to-video editing involves editing a source video along with additional
control (such as text prompts, subjects, or styles) to generate a new video
that aligns with the source video and the provided control. Traditional methods
have been constrained to certain editing types, limiting their ability to meet
the wide range of user demands. In this paper, we introduce AnyV2V, a novel
training-free framework designed to simplify video editing into two primary
steps: (1) employing an off-the-shelf image editing model (e.g.
InstructPix2Pix, InstantID, etc) to modify the first frame, (2) utilizing an
existing image-to-video generation model (e.g. I2VGen-XL) for DDIM inversion
and feature injection. In the first stage, AnyV2V can plug in any existing
image editing tools to support an extensive array of video editing tasks.
Beyond the traditional prompt-based editing methods, AnyV2V also can support
novel video editing tasks, including reference-based style transfer,
subject-driven editing, and identity manipulation, which were unattainable by
previous methods. In the second stage, AnyV2V can plug in any existing
image-to-video models to perform DDIM inversion and intermediate feature
injection to maintain the appearance and motion consistency with the source
video. On the prompt-based editing, we show that AnyV2V can outperform the
previous best approach by 35\% on prompt alignment, and 25\% on human
preference. On the three novel tasks, we show that AnyV2V also achieves a high
success rate. We believe AnyV2V will continue to thrive due to its ability to
seamlessly integrate the fast-evolving image editing methods. Such
compatibility can help AnyV2V to increase its versatility to cater to diverse
user demands.