  Automatic image matting (AIM) refers to estimating the soft foreground from
an arbitrary natural image without any auxiliary input like trimap, which is
useful for image editing. Prior methods try to learn semantic features to aid
the matting process while being limited to images with salient opaque
foregrounds such as humans and animals. In this paper, we investigate the
difficulties when extending them to natural images with salient
transparent/meticulous foregrounds or non-salient foregrounds. To address the
problem, a novel end-to-end matting network is proposed, which can predict a
generalized trimap for any image of the above types as a unified semantic
representation. Simultaneously, the learned semantic features guide the matting
network to focus on the transition areas via an attention mechanism. We also
construct a test set AIM-500 that contains 500 diverse natural images covering
all types along with manually labeled alpha mattes, making it feasible to
benchmark the generalization ability of AIM models. Results of the experiments
demonstrate that our network trained on available composite matting datasets
outperforms existing methods both objectively and subjectively. The source code
and dataset are available at https://github.com/JizhiziLi/AIM.
