Self-adjusting graph convolution UNet method for 3D human pose estimation
A few issues with the graph convolutional network-based 3D human pose estimation technique include the shared weight of all nodes,the incapacity to extract the multi-scale properties of the nodes,and the failure to take advantage of the topological relationships between neighboring nodes.In this paper,self-adjusting graph convolution UNet method for 3D human pose estimation(M-Joint-UNet)is proposed.M-Joint-UNet is composed of three parts:Joint-UNet,self-adjusting graph convolution,and fusion loss.Joint-UNet changes the size of the feature map through node pooling and unpooling to extract features of different scales of nodes.Self-adjusting graph convolution uses L1 and L2 fusion to mitigate gradient explosions and automatically modifies the relationship between neighboring nodes or the human skeletal structure using a learnable matrix.Comparative experiments show that the proposed method obtains optimal results in terms of the number of parameters and the estimation performance.With the 2D ground truth of Human3.6M as the input,the number of parameters is only 0.54×106,and the MPJPE and P-MPJPE values are 37.81 mm and 30.21 mm,respectively.
3D human pose estimationgraph convolutionGraph-UNetjoint poolweight matrix
万方数据
维普
