Abstract

With the recent growth of video-based Social Network Service (SNS) platforms, the demand for video editing among common users has increased. However, video editing can be challenging due to the temporally-varying factors such as camera movement and moving objects. While modern atlas-based video editing methods have addressed these issues, they often fail to edit videos including complex motion or multiple moving objects, and demand excessive computational cost, even for very simple edits. In this paper, we propose a novel region-of-interest (ROI)-based video editing framework: ROI-based Neural Atlas (RNA). Unlike prior work, RNA allows users to specify editing regions, simplifying the editing process by removing the need for foreground separation and atlas modeling for foreground objects. However, this simplification presents a unique challenge: acquiring a mask that effectively handles occlusions in the edited area caused by moving objects, without relying on an additional segmentation model. To tackle this, we propose a novel mask refinement approach designed for this specific challenge. Moreover, we introduce a soft neural atlas model for video reconstruction to ensure high-quality editing results. Extensive experiments show that RNA offers a more practical and efficient editing solution, applicable to a wider range of videos with superior quality compared to prior methods.  

Semi-Transparent Effects

Text2LIVE successfully augments the input scene with complex semi-transparent effects without changing irrelevant content in the image.

External Coverage

[Two Minute Papers]

Paper

Text2LIVE: Text-Driven Layered Image and Video Editing Omer Bar-Tal*, Dolev Ofri-Amar*, Rafail Fridman*, Yoni Kasten, Tali Dekel. (* indicates equal contribution) ECCV 2022 Oral. [paper]

 

Supplementary Material

[supplementary page]

 

Code

[code]

 

Bibtex

@article{lee2024rna,
   title={RNA: Video Editing with ROI-based Neural Atlas},
   author={Lee, Jaekyeong and Kim, Geonung and Cho, Sunghyun},
   journal={arXiv preprint arXiv:2410.07600},
   year={2024}
 }
			  

 

Acknowledgments

We thank Kfir Aberman, Lior Yariv, Shai Bagon, and Narek Tumanyan for their insightful comments. We thank Narek Tumanyan for his help with the baselines comparison.