The paper introduces a novel framework called Regional Multi-Person Pose Estimation (RMPE) to address the challenge of multi-person pose estimation in real-world scenarios. The framework consists of three main components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). These components work together to improve the accuracy of pose estimation when dealing with inaccurate human bounding boxes and redundant detections. The SSTN extracts high-quality single-person regions from bounding boxes, while the parallel SPPE branch optimizes this process. The parametric Pose NMS eliminates redundant poses using a novel pose distance metric, and the PGPG generates additional training samples by learning the conditional distribution of bounding box proposals for a given human pose. The RMPE framework is evaluated on the MPII (multi-person) dataset and achieves a 76.7 mAP, outperforming state-of-the-art methods. The paper also includes ablation studies to validate the effectiveness of each component and discusses potential future improvements.The paper introduces a novel framework called Regional Multi-Person Pose Estimation (RMPE) to address the challenge of multi-person pose estimation in real-world scenarios. The framework consists of three main components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). These components work together to improve the accuracy of pose estimation when dealing with inaccurate human bounding boxes and redundant detections. The SSTN extracts high-quality single-person regions from bounding boxes, while the parallel SPPE branch optimizes this process. The parametric Pose NMS eliminates redundant poses using a novel pose distance metric, and the PGPG generates additional training samples by learning the conditional distribution of bounding box proposals for a given human pose. The RMPE framework is evaluated on the MPII (multi-person) dataset and achieves a 76.7 mAP, outperforming state-of-the-art methods. The paper also includes ablation studies to validate the effectiveness of each component and discusses potential future improvements.