Adaptive Background Matting Using Background Matching
Jinlin Liu

TL;DR
This paper introduces an adaptive background matting technique that uses dynamic background matching and semantic estimation to achieve high-quality alpha mattes without static backgrounds or manual input, enhancing flexibility and stability.
Contribution
It proposes a novel adaptive background matting method utilizing background matching and semantic estimation networks for flexible, real-world video matting without static backgrounds or manual trimaps.
Findings
Performs comparably to state-of-the-art methods.
Handles dynamic backgrounds effectively.
Offers a flexible and convenient solution for real applications.
Abstract
Due to the difficulty of solving the matting problem, lots of methods use some kinds of assistance to acquire high quality alpha matte. Green screen matting methods rely on physical equipment. Trimap-based methods take manual interactions as external input. Background-based methods require a pre-captured, static background. The methods are not flexible and convenient enough to use widely. Trimap-free methods are flexible but not stable in complicated video applications. To be stable and flexible in real applications, we propose an adaptive background matting method. The user first captures their videos freely, moving the cameras. Then the user captures the background video afterwards, roughly covering the previous captured regions. We use dynamic background video instead of static background for accurate matting. The proposed method is convenient to use in any scenes as the static…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Vision and Imaging · Video Coding and Compression Technologies
