With recent advances in image and video diffusion models for content
creation, a plethora of techniques have been proposed for customizing their
generated content. In particular, manipulating the cross-attention layers of
Text-to-Image (T2I) diffusion models has shown great promise in controlling the
shape and location of objects in the scene. Transferring image-editing
techniques to the video domain, however, is extremely challenging as object
motion and temporal consistency are difficult to capture accurately. In this
work, we take a first look at the role of cross-attention in Text-to-Video
(T2V) diffusion models for zero-shot video editing. While one-shot models have
shown potential in controlling motion and camera movement, we demonstrate
zero-shot control over object shape, position and movement in T2V models. We
show that despite the limitations of current T2V models, cross-attention
guidance can be a promising approach for editing videos.

最新的图像和视频扩散模型在内容创作方面取得了显著进展，为定制生成内容提供了大量技术。本文首次研究了跨注意力在零样本视频编辑中的作用，展示了在 T2V 模型中实现对物体形状、位置和运动的控制的潜力。

研究跨注意力对解锁零 - shot 编辑的文本到视频扩散模型的效果

Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot  Editing of Text-to-Video Diffusion Models

The text-driven image and video diffusion models have achieved unprecedented
success in generating realistic and diverse content. Recently, the editing and
variation of existing images and videos in diffusion-based generative models
have garnered significant attention. However, previous works are limited to
editing content with text or providing coarse personalization using a single
visual clue, rendering them unsuitable for indescribable content that requires
fine-grained and detailed control. In this regard, we propose a generic video
editing framework called Make-A-Protagonist, which utilizes textual and visual
clues to edit videos with the goal of empowering individuals to become the
protagonists. Specifically, we leverage multiple experts to parse source video,
target visual and textual clues, and propose a visual-textual-based video
generation model that employs mask-guided denoising sampling to generate the
desired output. Extensive results demonstrate the versatile and remarkable
editing capabilities of Make-A-Protagonist.

该论文提出一个称为 Make-A-Protagonist 的通用视频编辑框架，其利用文本和视觉线索编辑视频，旨在使个人成为主角。