Deep learning for material synthesis and pose estimation material systems: A review

Abstract The estimation of human pose has been an important field of study for the area of computer vision. Due to its significant applications in various important fields such as human computer interaction, action recognition, video surveillance, threat prediction, etc., it has been in the focus of researchers mainly. Dramatic effects of deep learning techniques have resulted in major steps and important developments in estimating human poses. We are proposing a fast and efficient deep learning approach to detecting 2D pose from a monocular image by multiple persons. The method uses a Vector Field (PVF) part affinity, which learns how well the limbs have been connected. The architectural design provides a global pixel-level interpretation of the number of people by enabling a bottom-up approach that maintains high accuracy in real time. The architecture has been designed to use two branches of a convolutional neural network (CNN) with sequential prediction to understand and reference part locations around each other. Successfully, with the help of the MS COCO human pose dataset, we have implemented our approach.