  Image matting is an important computer vision problem. Many existing matting
methods require a hand-made trimap to provide auxiliary information, which is
very expensive and limits the real world usage. Recently, some trimap-free
methods have been proposed, which completely get rid of any user input.
However, their performance lag far behind trimap-based methods due to the lack
of guidance information. In this paper, we propose a matting method that use
Flexible Guidance Input as user hint, which means our method can use trimap,
scribblemap or clickmap as guidance information or even work without any
guidance input. To achieve this, we propose Progressive Trimap Deformation(PTD)
scheme that gradually shrink the area of the foreground and background of the
trimap with the training step increases and finally become a scribblemap. To
make our network robust to any user scribble and click, we randomly sample
points on foreground and background and perform curve fitting. Moreover, we
propose Semantic Fusion Module(SFM) which utilize the Feature Pyramid
Enhancement Module(FPEM) and Joint Pyramid Upsampling(JPU) in matting task for
the first time. The experiments show that our method can achieve
state-of-the-art results comparing with existing trimap-based and trimap-free
methods.
