FP-Net: Frontal Pose Estimation Based on Single Human Body Image withArbitrary View

Abstract

Accurate extraction of the frontal pose of the human body can assist behavior recognition, image generation, and virtually try-on work. However, side images and back images have problems, such as self-occlusion of the human body and invisible key points, which make the extraction of the frontal pose very difficult. Therefore, a frontal pose estimation network FP-Net (frontal pose network) based on single human body image at arbitrary view is designed and implemented. First, a multi-view human body image data set is produced to provide data support for model design. Second, in order to improve the accuracy of model prediction results, a regression module based on Anchor pose and a feature fusion module based on 3D pose are designed. Finally, FP-Net realizes the frontal pose extraction of human body images from arbitrary view. The PCK evaluation index is used for ablation experiments on the BJUT Taichi and CMU Panoptic datasets. The results show that proposed method effectively improves the accuracy of the frontal pose estimation of the human body.