Domain Adaptation Algorithm for 3D Human Pose Estimation with Spatial Attention and Position Optimization

Existing 3D human pose estimators perform well on a single dataset but are limited by the single pose structure of the training data, resulting in insufficient generalization to cross-domain experiments. Existing methods mitigate this deficiency by increasing pose diversity, but their generated poses often lack validity. Moreover, there is still a significant gap between the global positions of poses in the target and source domains. To address these issues, a spatial attention and global position optimization domain adaptation algorithm for 3D human pose estimation based on generative adversarial network (GAN) is proposed. The algorithm introduces a spatial node attention module to constrain the generator to produce more natural human poses, and combines it with a pose position correction module to drive the generated poses to align to the target data domain, thus solving the above domain adaptation problem. In addition, in order to improve the stability of estimator training, an end-to-end stochastic hybrid training strategy is proposed so that the pose estimator can take into account the learning of new and old data information. As a generative domain adaptation method, this algorithm can be efficiently applied to various two-stage 3D human pose estimators. Through cross-scene experiments and cross-dataset experiments, the results show that the proposed algorithm achieves the current best performance on several benchmark datasets. Among them, in the 3DHP dataset, the MPJPE and AUC metrics of the proposed method are optimized by 1.7% and 1.4% compared with the optimal work, which verifies that the proposed algorithm can effectively improve the generalization of 3D human pose estimators.