Abstract

Recent advancements in video generation have been remarkable, yet many existing methods struggle with issues of consistency and poor text-video alignment. Moreover, the field lacks effective techniques for text-guided video inpainting, a stark contrast to the well-explored domain of text-guided image inpainting. To this end, this paper proposes a novel text-guided video inpainting model that achieves better consistency, controllability and compatibility. Specifically, we introduce a simple but efficient motion capture module to preserve motion consistency, and design an instance-aware region selection instead of a random region selection to obtain better textual controllability, and utilize a novel strategy to inject some personalized models into our CoCoCo model and thus obtain better model compatibility. Extensive experiments show that our model can generate high-quality video clips. Meanwhile, our model shows better motion consistency, textual controllability and model compatibility.

Motion Capture Module

We propose a novel motion capture module. It consists of three types attention block, including two previously used temporal attention layers, a newly introduced damped global attention layer and a textual cross attention layer. The new motion capture module can enable the model to have better motion consistency and text-video controllability.

Instance-Aware Region Selection

We design a new instance-aware region selection strategy instead of a random mask selection strategy used in previous method. The new strategy can help the model achieve better text-video controllability.

T2I Model for Video Inpainting

We introduce a novel strategy to transform some personalized generation models, and then plug them into our our text-guided video inpainting model. This strategy can enhance the compatibility of our model.

Experiment

We compare our COCOCO with AVID by using same video samples.

@article{zi2024cococo,
  title={CoCoCo: Improving Text-Guided Video Inpainting for Better blue Consistency, Controllability and Compatibility},
  author={Zi, Bojia and Zhao, Shihao and Qi, Xianbiao and Wang, Jianan and Shi, Yukai and Chen, Qianyu and Liang, Bin and Wong, Kam-Fai and Zhang, Lei},
  journal={arXiv preprint arXiv:2403.12305},
  year={2024}
}