Extracting accurate foreground animals from natural animal images benefits many downstream applications such as film production and augmented reality. However, the various appearance and furry characteristics of animals 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 animal image matting. Besides, we establish a novel Animal Matting dataset (AM-2k) containing 2,000 high-resolution natural animal images from 20 categories along with manually labeled alpha mattes. Furthermore, we investigate the domain gap issue between composite images and natural images systematically by conducting comprehensive analyses of various discrepancies between 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. Comprehensive empirical studies on AM-2k demonstrate that GFM outperforms state-of-the-art methods and effectively reduces the generalization error.

本文提出了一种新颖的前景抠图模型GFM，采用了共享编码器和两个独立解码器，在高级语义分割和低级细节抠图之间分别进行学习，以实现自然图像的端到端抠图。同时，我们通过对合成图像和真实世界图像之间的区别进行了系统的分析，提出了一种RSSN组合路径，以便更好地提高模型的泛化能力。实验结果表明，GFM模型在真实世界图像上的泛化性能得到了有效提高。

连接复合与真实：朝向端到端的深度图像抠图