3D human pose estimation method based on multi-constrained dilated convolutions

In recent years, research on 2D to 3D human pose estimation methods has gained increasing attention. However, these methods, such as depth ambiguity and self-occlusion, still need to be addressed. To address these problems, we propose a 3D human pose estimation method based on multi-constrained dilated convolutions. This approach involves using a local constraint based on graph convolution and a global constraint based on a fully connected network. It also utilizes a dilated temporal convolution network to capture long-term temporal correlations of human poses. Taking 2D joint coordinate sequences as input, the local constraint module constructs cross-joint and equipotential connections for the human skeleton. The global constraint module encodes global semantic information about posture. Finally, the constraint modules and the temporal correlation of human posture are alternately connected to achieve 3D human posture estimation. The method was validated on the public datasets Human3.6M and MPI-INF-3DHP, and the results show that the proposed method effectively reduces the error in 3D human pose estimation and demonstrates a certain degree of generalization ability.