Object-Wise Video Editing

Abstract

Beyond time frame editing in video data, object level video editing is a challenging task; such as object removal in a video or viewpoint changes. These tasks involve dynamic object segmentation, novel view video synthesis and background inpainting. Background inpainting is a task of the reconstruction of unseen regions presented by object removal or viewpoint change. In this paper, we propose a video editing method including foreground object removal background inpainting and novel view video synthesis under challenging conditions such as complex visual pattern, occlusion, overlaid clutter and variation of depth in a moving camera. Our proposed method calculates a weighted confidence score on the basis of normalized difference between observed depth and predicted distance in 3D space. A set of potential points from epipolar lines from neighbor frames are collected, refined, and weighted to select a few number of highly qualified observations to fill the desired region of interest area in the current frame from video. Based on the background inpainting method, novel view video synthesis is conducted with arbitrary viewpoint. Our method is evaluated with both a public dataset and our own video clips and compared with multiple state of the art methods showing a superior performance.

Distribution-Transformed Network for Impulse Noise Removal

“…U NDERSTANDING videos [1], [2] such as TV series and movies has been a prior step to various vision tasks such as story understanding [3]- [5], browsing movie collections [6], [7], and identity-based video editing [1], [2]. However, it relies on identifying the characters and analyzing behaviors, considering characters are always core elements of any story.…”