We introduce a novel and efficient approach for text-based video-to-video
editing that eliminates the need for resource-intensive per-video-per-model
finetuning. At the core of our approach is a synthetic paired video dataset
tailored for video-to-video transfer tasks. Inspired by Instruct Pix2Pix's
image transfer via editing instruction, we adapt this paradigm to the video
domain. Extending the Prompt-to-Prompt to videos, we efficiently generate
paired samples, each with an input video and its edited counterpart. Alongside
this, we introduce the Long Video Sampling Correction during sampling, ensuring
consistent long videos across batches. Our method surpasses current methods
like Tune-A-Video, heralding substantial progress in text-based video-to-video
editing and suggesting exciting avenues for further exploration and deployment.

一项针对基于文本的视频编辑的新型高效方法，通过自动生成适用于视频转换任务的合成配对视频数据集，用于视频图像编辑指令的转换，并采用长视频采样校正以确保一致性。该方法超越了现有的方法（如 Tune-A-Video），在基于文本的视频编辑方面取得了显著进展，并提出了更多领域探索和应用的激动人心的方向。