AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks
<|reference_start|>AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks: 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.<|reference_end|>
@article{ku2024anyv2v:,
    title={AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks},
    author={Max Ku and Cong Wei and Weiming Ren and Harry Yang and Wenhu Chen},
    journal={arXiv preprint arXiv:2403.14468},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14468},
    primaryClass={cs.CV cs.AI cs.MM}
}