MPPose: An Efficient Multi-Path Network for 2D Human Pose Estimation

Human pose estimation models are increasingly deployed on low-computation devices, with extensive applications in motion capture and sports rehabilita-tion. The multi-scale feature extraction capability of high-resolution networks HRNet effectively addresses the issue of varying human body scales, enhanc-ing the accuracy of lightweight models based on HRNet. However, the high-resolution architecture results in a more complex network structure and in-creased computational overhead. This paper introduces MPPose, a top-down human pose estimation framework that integrates coordinate classification based on keypoint heatmap representation. We design a single-branch network based on a high-resolution architecture, which implicitly retains and fuses mul-ti-scale features. The multi-path network maintains both the simplicity of sin-gle-branch network and the effectiveness of high-resolution network, resulting in a simpler and more efficient architecture. Based on the high-resolution architectures, we retain only the blocks in the lowest-resolution branch and employ both cross-resolution and same-resolution feature fusion. We redesign an efficient block inspired by the shuf-fle block, which we called the Channel Expansion Attention Module CEAM . CEAM compensates for the reduction in channel information caused by chan-nel splitting by introducing a channel scaling module and a channel attention module. We evaluate our model against state-of-the-art top-down methods on the COCO and MPII datasets. Results show that it reduces computational overhead by 20 and improves inference speed by 37 , while achieving accu-racy on par with Lite-HRNet.