An Adversarial Human Pose Estimation Network Injected with Graph Structure
This work addresses a domain-specific issue in computer vision for human pose estimation, offering an incremental improvement over existing methods.
The paper tackles the problem of inaccurate human pose estimation due to invisible keypoints by proposing a novel GAN with Cascade Feature and Graph Structure Networks, achieving improved localization accuracy on LSP, MPII, and COCO datasets.
Because of the invisible human keypoints in images caused by illumination, occlusion and overlap, it is likely to produce unreasonable human pose prediction for most of the current human pose estimation methods. In this paper, we design a novel generative adversarial network (GAN) to improve the localization accuracy of visible joints when some joints are invisible. The network consists of two simple but efficient modules, Cascade Feature Network (CFN) and Graph Structure Network (GSN). First, the CFN utilizes the prediction maps from the previous stages to guide the prediction maps in the next stage to produce accurate human pose. Second, the GSN is designed to contribute to the localization of invisible joints by passing message among different joints. According to GAN, if the prediction pose produced by the generator G cannot be distinguished by the discriminator D, the generator network G has successfully obtained the underlying dependence of human joints. We conduct experiments on three widely used human pose estimation benchmark datasets, LSP, MPII and COCO, whose results show the effectiveness of our proposed framework.