Text-based video editing has recently attracted considerable interest in
changing the style or replacing the objects with a similar structure. Beyond
this, we demonstrate that properties such as shape, size, location, motion,
etc., can also be edited in videos. Our key insight is that the keyframe
transformations of the specific internal feature (e.g., edge maps of objects or
human pose), can easily propagate to other frames to provide generation
guidance. We thus propose MagicStick, a controllable video editing method that
edits the video properties by utilizing the transformation on the extracted
internal control signals. In detail, to keep the appearance, we inflate both
the pretrained image diffusion model and ControlNet to the temporal dimension
and train low-rank adaptions (LORA) layers to fit the specific scenes. Then, in
editing, we perform an inversion and editing framework. Differently, finetuned
ControlNet is introduced in both inversion and generation for attention
guidance with the proposed attention remix between the spatial attention maps
of inversion and editing. Yet succinct, our method is the first method to show
the ability of video property editing from the pre-trained text-to-image model.
We present experiments on numerous examples within our unified framework. We
also compare with shape-aware text-based editing and handcrafted motion video
generation, demonstrating our superior temporal consistency and editing
capability than previous works. The code and models will be made publicly
available.

最近文字导向的视频编辑引起了极大的兴趣，我们提出了 MagicStick 方法，通过利用提取的内部控制信号的转换，来对视频属性进行编辑，展示了对形状、大小、位置、动作等进行编辑的能力，我们的方法是第一个展示了从预训练文字到图像模型的视频属性编辑能力，与基于形状的文字导向编辑和手工制作的动态视频生成方法相比，我们展示了优于以往方法的时间一致性和编辑能力。

MagicStick: 可控视频编辑通过控制杆转换

MagicStick: Controllable Video Editing via Control Handle  Transformations

Motivated by the superior performance of image diffusion models, more and
more researchers strive to extend these models to the text-based video editing
task. Nevertheless, current video editing tasks mainly suffer from the dilemma
between the high fine-tuning cost and the limited generation capacity. Compared
with images, we conjecture that videos necessitate more constraints to preserve
the temporal consistency during editing. Towards this end, we propose EVE, a
robust and efficient zero-shot video editing method. Under the guidance of
depth maps and temporal consistency constraints, EVE derives satisfactory video
editing results with an affordable computational and time cost. Moreover,
recognizing the absence of a publicly available video editing dataset for fair
comparisons, we construct a new benchmark ZVE-50 dataset. Through comprehensive
experimentation, we validate that EVE could achieve a satisfactory trade-off
between performance and efficiency. We will release our dataset and codebase to
facilitate future researchers.

通过深度图和时序一致性约束，提出了一种强大且高效的零样本视频编辑方法 EVE，能够以可承受的计算和时间成本派生出令人满意的视频编辑结果，并构建了新的基准 ZVE-50 数据集以促进未来研究。