Can I read NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing on EtoBox?

NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing by Chen, Ting-Hsuan; Chan, Jiewen; Shiu, Hau-Shiang; Yen, Shih-Han; Yeh, Chang-Han; Liu, Yu-Lun is a scholarly article available to read on EtoBox.

What is NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing about?

We propose a video editing framework, NaRCan, which integrates a hybrid deformation field and diffusion prior to generate high-quality natural canonical images to represent the input video. Our approach utilizes homography to model global motion and employs multi-layer perceptrons (MLPs) to capture local residual deformations, enhancing the model's ability to handle complex video dynamics. By introducing a diffusion prior from the early stages of training, our model ensures that the generated images retain a high-quality natural appearance, making the produced canonical images suitable for various downstream tasks in video editing, a capability not achieved by current canonical-based methods. Furthermore, we incorporate low-rank adaptation (LoRA) fine-tuning and introduce a noise and diffusion prior update scheduling technique that accelerates the training process by 14 times. Extensive experimental results show that our method outperforms existing approaches in various video editing tasks and produces coherent and high-quality edited video sequences. See our project page for video results at https://koi953215.github.io/NaRCan_page/.

Author

Chen, Ting-Hsuan; Chan, Jiewen; Shiu, Hau-Shiang; Yen, Shih-Han; Yeh, Chang-Han; Liu, Yu-Lun

Published

2024

Language

EN