  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.
