3D Human Pose Estimation Based on a Fully Connected Neural Network With Adversarial Learning Prior Knowledge

Abstract

3D human pose estimation is more and more widely used in the real world, such as sports guidance, limb rehabilitation training, augmented reality, and intelligent security. Most existing human pose estimation methods are designed based on an RGB image obtained by one optical sensor, such as a digital camera. There is some prior knowledge, such as bone proportion and angle limitation of joint hinge motion. However, the existing methods do not consider the correlation between different joints from multi-view images, and most of them adopt fixed spatial prior constraints, resulting in poor generalizations. Therefore, it is essential to build a multi-view image acquisition system using optical sensors and customized algorithms for a 3D reconstruction of the human pose in the image. Inspired by generative adversarial networks (GAN), we used a data-driven method to learn the implicit spatial prior information and classified joints according to the natural connection characteristics. To accelerate the proposed method, we proposed a fully connected network with skip connections and used the SMPL model to make the 3D human body reconstruction. Experimental results showed that compared with other state-of-the-art methods, the joints’ average error of the proposed method was the smallest, which indicated the best performance. Moreover, the running time of the proposed method was 1.3 seconds per frame, which may not meet real-time requirements, but is still much faster than most existing methods.