3D human pose estimation is a hot topic in the field of computer vision. It provides data support for tasks such as pose recognition, human tracking and action recognition. Therefore, it is widely applied in the fields of advanced human-computer interaction, intelligent monitoring and so on. Estimating 3D human pose from a single 2D image is an ill-posed problem and is likely to cause low prediction accuracy, due to the problems of self-occlusion and depth ambiguity. This paper developed two types of human kinematics to improve the estimation accuracy. First, taking the 2D human body skeleton sequence obtained by the 2D human body pose detector as input, a temporal convolutional network is proposed to develop the movement periodicity in temporal domain. Second, geometrical prior knowledge is introduced into the model to constrain the estimated pose to fit the general kinematics knowledge. The experiments are tested on Human3.6M and MPII (Max Planck Institut Informatik) Human Pose (MPI-INF-3DHP) datasets, and the proposed model shows better generalization ability compared with the baseline and the state-of-the-art models.

Three-dimensional human pose estimation is a key technology in many computer vision tasks. Regressing a 3D pose from 2D images is a challenging task, especially for applications in natural scenes. Recovering the 3D pose from a monocular image is an ill-posed problem itself; moreover, most of the existing datasets have been captured in a laboratory environment, which means that the model trained by them cannot generalize well to in-the-wild data. In this work, we improve the 3D pose estimation performance by introducing the attention mechanism and a calibration network. The attention model will capture the channel-wise dependence, so as to enhance the depth analysis ability of the model. The multi-scale pose calibration network adaptively learns body structure and motion characteristics, and will therefore rectify the estimation results. We tested our model on the Human 3.6M dataset for quantitive evaluation, and the experimental results show the proposed methods with higher accuracy. In order to test the generalization capability for in-the-wild applications, we also report the qualitative results on the natural scene Leeds Sports Pose dataset; the visualization results show that the estimated results are more reasonable than the baseline model.