alpha matting, human images, deep learning, pose estimation
We propose a novel end-to-end deep learning framework, the Joint Matting Network (JMNet), to automatically generate alpha mattes for human images. We utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module, which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-the-art human matting techniques; the shared encoder leads to better performance and lower memory costs. Our model can process real images downloaded from the Internet for use in composition applications.
Tsinghua University Press
Xian Wu, Xiao-Nan Fang, Tao Chen et al. JMNet: A joint matting network for automatic human matting. Computational Visual Media 2020, 6(2): 215-224.