Attention-Guided Video Inference for 3D Human posture estimation

Abstract

Body posture refers to the body shape of the human body during exercise, which reflects the external shape and mental state of the body, and is a description of physical health. If you want to use artificial intelligence to understand human posture. Human pose estimation based on a single image is the foundation. Estimating human pose from monocular videos is still a challenging task. It has three main challenges, including spatial feature representation, temporal information representation, and model computational complexity. To this end, we propose an improved Video Inference for Human Body Pose and Shape Estimation. It can perform feature extraction in the temporal dimension in image sequences. We also define a new temporal network architecture with a self-attention mechanism, and perform feature extraction on the temporal dimension in the original framework. In order to improve, extensive experiments prove that the method has a significant improvement in challenging 3D pose estimation datasets and can more effectively extract the features of human poses from videos.