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arXiv · 2101.02391

Multi-scale Information Assembly for Image Matting

Yu Qiao·Yuhao Liu·Qiang Zhu·Xin Yang·Yuxin Wang·Qiang Zhang·Xiaopeng Wei
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Abstract

Image matting is a long-standing problem in computer graphics and vision, mostly identified as the accurate estimation of the foreground in input images. We argue that the foreground objects can be represented by different-level information, including the central bodies, large-grained boundaries, refined details, etc. Based on this observation, in this paper, we propose a multi-scale information assembly framework (MSIA-matte) to pull out high-quality alpha mattes from single RGB images. Technically speaking, given an input image, we extract advanced semantics as our subject content and retain initial CNN features to encode different-level foreground expression, then combine them by our well-designed information assembly strategy. Extensive experiments can prove the effectiveness of the proposed MSIA-matte, and we can achieve state-of-the-art performance compared to most existing matting networks.

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Yu Qiao, Yuhao Liu, Qiang Zhu, Xin Yang, Yuxin Wang, Qiang Zhang, Xiaopeng Wei. 2021-01-07. Multi-scale Information Assembly for Image Matting. https://doi.org/10.1111/cgf.14168

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