This paper proposes a novel regional multi-person pose estimation (RMPE) framework to improve pose estimation in the presence of inaccurate human bounding boxes. The framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). SSTN is used to extract high-quality single-person regions from inaccurate bounding boxes, while the parallel SPPE branch helps refine the pose estimation. The parametric pose NMS eliminates redundant poses by comparing pose similarity using a novel distance metric, and the PGPG generates additional training samples by learning the distribution of bounding box offsets for different poses. The RMPE framework is general and can be applied to different human detectors and single-person pose estimators. It achieves 76.7 mAP on the MPII (multi-person) dataset, outperforming the state-of-the-art methods. The framework is validated through ablation studies, which show that each component contributes significantly to the performance. The framework is also tested on the MSCOCO Keypoints Challenge dataset, achieving state-of-the-art results. The RMPE framework addresses the challenges of multi-person pose estimation in the wild, where human detectors may produce inaccurate bounding boxes. The framework improves the performance of single-person pose estimation algorithms by handling inaccurate bounding boxes and redundant detections. The framework is effective in handling cases where human detectors fail to accurately locate individuals, and it can also handle cases where multiple people are closely positioned, leading to ambiguous pose estimation. The framework is also effective in handling cases where the human detector produces incorrect bounding boxes, as it can still accurately estimate the pose of the individual. The framework is implemented using a two-step approach, where the first step involves detecting human bounding boxes, and the second step involves estimating the pose within each bounding box. The framework is trained using a combination of the SSTN and parallel SPPE modules, which help refine the pose estimation. The framework is also tested with different human detectors and pose estimators, demonstrating its generalizability. The framework is effective in handling a wide range of pose estimation tasks, including those with occlusions and overlapping individuals. The framework is also effective in handling cases where the human detector produces incorrect bounding boxes, as it can still accurately estimate the pose of the individual. The framework is effective in handling cases where multiple people are closely positioned, leading to ambiguous pose estimation. The framework is also effective in handling cases where the human detector produces incorrect bounding boxes, as it can still accurately estimate the pose of the individual.This paper proposes a novel regional multi-person pose estimation (RMPE) framework to improve pose estimation in the presence of inaccurate human bounding boxes. The framework consists of three components: Symmetric Spatial Transformer Network (SSTN), Parametric Pose Non-Maximum-Suppression (NMS), and Pose-Guided Proposals Generator (PGPG). SSTN is used to extract high-quality single-person regions from inaccurate bounding boxes, while the parallel SPPE branch helps refine the pose estimation. The parametric pose NMS eliminates redundant poses by comparing pose similarity using a novel distance metric, and the PGPG generates additional training samples by learning the distribution of bounding box offsets for different poses. The RMPE framework is general and can be applied to different human detectors and single-person pose estimators. It achieves 76.7 mAP on the MPII (multi-person) dataset, outperforming the state-of-the-art methods. The framework is validated through ablation studies, which show that each component contributes significantly to the performance. The framework is also tested on the MSCOCO Keypoints Challenge dataset, achieving state-of-the-art results. The RMPE framework addresses the challenges of multi-person pose estimation in the wild, where human detectors may produce inaccurate bounding boxes. The framework improves the performance of single-person pose estimation algorithms by handling inaccurate bounding boxes and redundant detections. The framework is effective in handling cases where human detectors fail to accurately locate individuals, and it can also handle cases where multiple people are closely positioned, leading to ambiguous pose estimation. The framework is also effective in handling cases where the human detector produces incorrect bounding boxes, as it can still accurately estimate the pose of the individual. The framework is implemented using a two-step approach, where the first step involves detecting human bounding boxes, and the second step involves estimating the pose within each bounding box. The framework is trained using a combination of the SSTN and parallel SPPE modules, which help refine the pose estimation. The framework is also tested with different human detectors and pose estimators, demonstrating its generalizability. The framework is effective in handling a wide range of pose estimation tasks, including those with occlusions and overlapping individuals. The framework is also effective in handling cases where the human detector produces incorrect bounding boxes, as it can still accurately estimate the pose of the individual. The framework is effective in handling cases where multiple people are closely positioned, leading to ambiguous pose estimation. The framework is also effective in handling cases where the human detector produces incorrect bounding boxes, as it can still accurately estimate the pose of the individual.