Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation

Understand

For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions.

• Under these circumstances, biologically implausible pose predictions may be produced.
• In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity.
• To address the problem by incorporating priors about the structure of human bodies, we propose a novel structure-aware convolutional network to implicitly take such priors into account during training of the deep network.