Recent endeavors in video editing have showcased promising results in
single-attribute editing or style transfer tasks, either by training
text-to-video (T2V) models on text-video data or adopting training-free
methods. However, when confronted with the complexities of multi-attribute
editing scenarios, they exhibit shortcomings such as omitting or overlooking
intended attribute changes, modifying the wrong elements of the input video,
and failing to preserve regions of the input video that should remain intact.
To address this, here we present a novel grounding-guided video-to-video
translation framework called Ground-A-Video for multi-attribute video editing.
Ground-A-Video attains temporally consistent multi-attribute editing of input
videos in a training-free manner without aforementioned shortcomings. Central
to our method is the introduction of Cross-Frame Gated Attention which
incorporates groundings information into the latent representations in a
temporally consistent fashion, along with Modulated Cross-Attention and optical
flow guided inverted latents smoothing. Extensive experiments and applications
demonstrate that Ground-A-Video's zero-shot capacity outperforms other baseline
methods in terms of edit-accuracy and frame consistency. Further results and
codes are provided at our project page (this http URL).

一种名为 Ground-A-Video 的新型基于 groundings 的多属性视频编辑框架，通过引入跨帧门控注意力、调制交叉注意力和光流引导反转隐藏特征平滑，实现了无需训练的时间一致的多属性视频编辑，并在编辑准确度和帧一致性方面表现优于其他基准方法。