Instance Segmentation based Semantic Matting for Compositing Applications

TL;DR

This paper introduces a fully automated method combining instance segmentation and image matting to enable high-quality, semantic-aware compositing in complex natural scenes without user interaction.

Contribution

It presents a novel integrated approach that refines instance segmentation and automates semantic image matting for natural scene compositing.

Findings

Improved performance over existing methods

Automated process without user interaction

Effective in complex natural backgrounds

Abstract

Image compositing is a key step in film making and image editing that aims to segment a foreground object and combine it with a new background. Automatic image compositing can be done easily in a studio using chroma-keying when the background is pure blue or green. However, image compositing in natural scenes with complex backgrounds remains a tedious task, requiring experienced artists to hand-segment. In order to achieve automatic compositing in natural scenes, we propose a fully automated method that integrates instance segmentation and image matting processes to generate high-quality semantic mattes that can be used for image editing task. Our approach can be seen both as a refinement of existing instance segmentation algorithms and as a fully automated semantic image matting method. It extends automatic image compositing techniques such as chroma-keying to scenes with complex natural backgrounds without the need for any kind of user interaction. The output of our approach can be considered as both refined instance segmentations and alpha mattes with semantic meanings. We provide experimental results which show improved performance results as compared to existing approaches.