  Extracting accurate foregrounds from natural images benefits many downstream
applications such as film production and augmented reality. However, the furry
characteristics and various appearance of the foregrounds, e.g., animal and
portrait, challenge existing matting methods, which usually require extra user
inputs such as trimap or scribbles. To resolve these problems, we study the
distinct roles of semantics and details for image matting and decompose the
task into two parallel sub-tasks: high-level semantic segmentation and
low-level details matting. Specifically, we propose a novel Glance and Focus
Matting network (GFM), which employs a shared encoder and two separate decoders
to learn both tasks in a collaborative manner for end-to-end natural image
matting. Besides, due to the limitation of available natural images in the
matting task, previous methods typically adopt composite images for training
and evaluation, which result in limited generalization ability on real-world
images. In this paper, we investigate the domain gap issue between composite
images and real-world images systematically by conducting comprehensive
analyses of various discrepancies between the foreground and background images.
We find that a carefully designed composition route RSSN that aims to reduce
the discrepancies can lead to a better model with remarkable generalization
ability. Furthermore, we provide a benchmark containing 2,000 high-resolution
real-world animal images and 10,000 portrait images along with their manually
labeled alpha mattes to serve as a test bed for evaluating matting model's
generalization ability on real-world images. Comprehensive empirical studies
have demonstrated that GFM outperforms state-of-the-art methods and effectively
reduces the generalization error. The code and the datasets will be released at
https://github.com/JizhiziLi/GFM.
