In this report, we present MagicEdit, a surprisingly simple yet effective
solution to the text-guided video editing task. We found that high-fidelity and
temporally coherent video-to-video translation can be achieved by explicitly
disentangling the learning of content, structure and motion signals during
training. This is in contradict to most existing methods which attempt to
jointly model both the appearance and temporal representation within a single
framework, which we argue, would lead to degradation in per-frame quality.
Despite its simplicity, we show that MagicEdit supports various downstream
video editing tasks, including video stylization, local editing, video-MagicMix
and video outpainting.

MagicEdit 是一种非常简单但非常有效的解决文本指导下的视频编辑任务的方法，通过在训练过程中明确分离内容、结构和动作信号的学习，可以实现高保真度和时间连贯性的视频到视频的转换。这与大多数现有方法试图在单个框架内同时建模外观和时间表示相矛盾，我们认为这会导致每帧质量下降。尽管简单，但我们展示了 MagicEdit 支持各种下游视频编辑任务，包括视频风格化、局部编辑、视频混合和视频外涂。