Name
Abstract | ||
|---|---|---|
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-theart 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s41095-020-0168-6 | Computational Visual Media |
Keywords | DocType | Volume |
alpha matting, human images, deep learning, pose estimation | Journal | 6 |
Issue | ISSN | Citations |
2 | 2096-0433 | 2 |
PageRank | References | Authors |
0.37 | 21 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xian Wu | 1 | 18 | 3.00 |
| Xiao-Nan Fang | 2 | 2 | 0.71 |
| Tao Chen | 3 | 99 | 18.12 |
| Fang-Lue Zhang | 4 | 269 | 15.60 |