Recent advances in text-guided video editing have showcased promising results
in appearance editing (e.g., stylization). However, video motion editing in the
temporal dimension (e.g., from eating to waving), which distinguishes video
editing from image editing, is underexplored. In this work, we present UniEdit,
a tuning-free framework that supports both video motion and appearance editing
by harnessing the power of a pre-trained text-to-video generator within an
inversion-then-generation framework. To realize motion editing while preserving
source video content, based on the insights that temporal and spatial
self-attention layers encode inter-frame and intra-frame dependency
respectively, we introduce auxiliary motion-reference and reconstruction
branches to produce text-guided motion and source features respectively. The
obtained features are then injected into the main editing path via temporal and
spatial self-attention layers. Extensive experiments demonstrate that UniEdit
covers video motion editing and various appearance editing scenarios, and
surpasses the state-of-the-art methods. Our code will be publicly available.

UniEdit 是一个无需调整参数的框架，它利用预先训练的文本到视频生成器，通过反演和生成的方式来支持视频运动和外观编辑。通过引入辅助运动参考和重建分支，利用时间和空间自注意层来实现运动编辑并保留源视频内容，UniEdit 在视频运动编辑和各种外观编辑场景上优于现有的方法。

UniEdit: 视频运动与外观编辑的统一无调节框架

UniEdit: A Unified Tuning-Free Framework for Video Motion and Appearance  Editing

Humans watch more than a billion hours of video per day. Most of this video
was edited manually, which is a tedious process. However, AI-enabled
video-generation and video-editing is on the rise. Building on text-to-image
models like Stable Diffusion and Imagen, generative AI has improved
dramatically on video tasks. But it's hard to evaluate progress in these video
tasks because there is no standard benchmark. So, we propose a new dataset for
text-guided video editing (TGVE), and we run a competition at CVPR to evaluate
models on our TGVE dataset. In this paper we present a retrospective on the
competition and describe the winning method. The competition dataset is
available at this https URL

利用 AI 实现文本引导下的视频编辑的方法在该研究中进行了描绘和竞赛评估，并提出了一种新的数据集用于文本引导下的视频编辑任务。

CVPR 2023 文本引导的视频编辑竞赛

CVPR 2023 Text Guided Video Editing Competition

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。