3D Human Posture Estimation with Dual Attention and Posture Enhancement
Although 3D human pose estimation has been developing at a high speed,the existing 3D human pose estimation models have a weak ability to distinguish features,and cannot effectively obtain multi-channel and spatial feature information,and the simulation effect is affected.To this end,this paper uses VideoPose3D and PoseAug as the basic networks to improve and get an efficient attitude estimation network CSNET that integrates dual attention and gesture enhancement.By integrating channel attention and spatial attention into the pose estimator combined with PoseAug,the CS block module is constructed as the basic module to improve the accuracy and comprehensiveness of features,so as to deal with the problems such as occlusion and uncertainty of depth.The verification and test on the public data set Human 3.6m show that compared with the original model,the average joint position error of this meth-od is reduced by 3.4%,and the accuracy of the 3D human pose estimation model is improved.The improved algo-rithm is used to identify the key points of the human body and drive the virtual character simulation to obtain better simulation results.
Human pose estimationChannel attentionSpatial attentionPose augmentationVirtual characters
NETL
NSTL
万方数据
