Aiming at the lack of depth information and incomplete spatial structure information of behavior and posture in 2D images,a 3D human joint point recognition method based on weak supervised migration network is proposed.Firstly,an end-to-end 3D human pose estimation framework for real images is proposed.The depth neural network is trained with 2D and 3D mixed label images.In the 2D human pose recognition sub network,the depth regression module is added to improve the 2D human pose recognition sub network to solve the problem of depth ambiguity in 3D human pose recognition;Secondly,in the 3D human pose recognition sub network,3D geometric constraints are introduced to standardize the human pose recognition.For the case of no real depth label,it can better learn the depth features and effectively solve the problem of human pose recognition with occlusion.In human 3.6m and mpii data sets,the average error of joint point prediction is lower than that of other methods,and has better 3D human posture recognition effect.
维普
万方数据