# DAOVI: Distortion-Aware Omnidirectional Video Inpainting

## TL;DR

This paper introduces DAOVI, a deep learning model specifically designed for inpainting omnidirectional videos, effectively handling geometric distortions and preserving spatial-temporal consistency.

## Contribution

DAOVI is the first inpainting model that explicitly accounts for distortion in equirectangular projections of omnidirectional videos, incorporating geodesic-aware motion and depth-aware feature propagation modules.

## Key findings

- DAOVI outperforms existing inpainting methods quantitatively.
- DAOVI produces more visually coherent inpainted omnidirectional videos.
- The model effectively handles geometric distortions in equirectangular projections.

## Abstract

Omnidirectional videos that capture the entire surroundings are employed in a variety of fields such as VR applications and remote sensing. However, their wide field of view often causes unwanted objects to appear in the videos. This problem can be addressed by video inpainting, which enables the natural removal of such objects while preserving both spatial and temporal consistency. Nevertheless, most existing methods assume processing ordinary videos with a narrow field of view and do not tackle the distortion in equirectangular projection of omnidirectional videos. To address this issue, this paper proposes a novel deep learning model for omnidirectional video inpainting, called Distortion-Aware Omnidirectional Video Inpainting (DAOVI). DAOVI introduces a module that evaluates temporal motion information in the image space considering geodesic distance, as well as a depth-aware feature propagation module in the feature space that is designed to address the geometric distortion inherent to omnidirectional videos. The experimental results demonstrate that our proposed method outperforms existing methods both quantitatively and qualitatively.