  Image matting refers to the estimation of the opacity of foreground objects.
It requires correct contours and fine details of foreground objects for the
matting results. To better accomplish human image matting tasks, we propose the
Cascade Image Matting Network with Deformable Graph Refinement, which can
automatically predict precise alpha mattes from single human images without any
additional inputs. We adopt a network cascade architecture to perform matting
from low-to-high resolution, which corresponds to coarse-to-fine optimization.
We also introduce the Deformable Graph Refinement (DGR) module based on graph
neural networks (GNNs) to overcome the limitations of convolutional neural
networks (CNNs). The DGR module can effectively capture long-range relations
and obtain more global and local information to help produce finer alpha
mattes. We also reduce the computation complexity of the DGR module by
dynamically predicting the neighbors and apply DGR module to higher--resolution
features. Experimental results demonstrate the ability of our CasDGR to achieve
state-of-the-art performance on synthetic datasets and produce good results on
real human images.
