Exécution de notre outil de synthèse sur un nouvel article

Résumé : Reliable human parts segmentation on 2D images plays an important role in many human-centric computer vision tasks. While significant achievements have been made on human pose estimation, the performance on human parts segmentation remains low. In this paper, we present a novel technique that we call Pose2Body that robustly conducts human parts segmentation based on the pose estimation results. We partition an image into superpixels and set out to assign a segment label to each superpixel most consistent with the pose. We design special feature vectors for every superpixel-label assignment as well as superpixel-superpixel pairs and model optimal labeling as to solve for a conditional random field (CRF). Comprehensive experiments show that our technique achieves substantial improvements over the state-of-the-art solutions.