Paper Info
Title
Improved Image Matting via Real-time User Clicks and Uncertainty Estimation
Abstract
Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high quality trimap itself is arduous, thus restricting the application of these methods. Recently, some trimap free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, without the trimap guidance in some cases, the target network is ambiguous about which is the foreground target. In fact, choosing the foreground is a subjective procedure and depends on the user's intention. To this end, this paper proposes an improved deep image matting framework which is trimap free and only needs several user click interactions to eliminate the ambiguity. Moreover, we introduce a new uncertainty estimation module that can predict which parts need polishing and a following local refinement module. Based on the computation budget, users can choose how many local parts to improve with the uncertainty guidance. Quantitative and qualitative results show that our method performs better than existing trimap free methods and comparably to state-of-the-art trimap-based methods with minimal user effort.
Year
DOI
Venue
2021
10.1109/CVPR46437.2021.01512
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Tianyi Wei100.34
Dongdong Chen25219.10
Wenbo Zhou3296.28
Jing Liao418225.81
Hanqing Zhao545.83
Weiming Zhang6110488.72
Nenghai Yu72238183.33