Neutral Editing Framework for Diffusion-based Video Editing
Sunjae Yoon Gwanhyeong Koo Ji Woo Hong Chang D. Yoo
Text-conditioned image editing has succeeded in various types of editing
based on a diffusion framework. Unfortunately, this success did not carry over
to a video, which continues to be challenging. Existing video editing systems
are still limited to rigid-type editing such as style transfer and object
overlay. To this end, this paper proposes Neutral Editing (NeuEdit) framework
to enable complex non-rigid editing by changing the motion of a person/object
in a video, which has never been attempted before. NeuEdit introduces a concept
of `neutralization' that enhances a tuning-editing process of diffusion-based
editing systems in a model-agnostic manner by leveraging input video and text
without any other auxiliary aids (e.g., visual masks, video captions).
Extensive experiments on numerous videos demonstrate adaptability and
effectiveness of the NeuEdit framework. The website of our work is available
here: https://neuedit.github.io