ControlVideo: Adding Conditional Control for One Shot Text-to-Video
  Editing
Authors: name: Min Zhao, name: Rongzhen Wang, name: Fan Bao, name: Chongxuan Li, name: Jun Zhu

In this paper, we present ControlVideo, a novel method for text-driven video
editing. Leveraging the capabilities of text-to-image diffusion models and
ControlNet, ControlVideo aims to enhance the fidelity and temporal consistency
of videos that align with a given text while preserving the structure of the
source video. This is achieved by incorporating additional conditions such as
edge maps, fine-tuning the key-frame and temporal attention on the source
video-text pair with carefully designed strategies. An in-depth exploration of
ControlVideo's design is conducted to inform future research on one-shot tuning
video diffusion models. Quantitatively, ControlVideo outperforms a range of
competitive baselines in terms of faithfulness and consistency while still
aligning with the textual prompt. Additionally, it delivers videos with high
visual realism and fidelity w.r.t. the source content, demonstrating
flexibility in utilizing controls containing varying degrees of source video
information, and the potential for multiple control combinations. The project
page is available at
https://ml.cs.tsinghua.edu.cn/controlvideo/{https://ml.cs.tsinghua.edu.cn/controlvideo/}.