  Real-time 3D human pose estimation is crucial for human-computer interaction.
It is cheap and practical to estimate 3D human pose only from monocular video.
However, recent bone splicing based 3D human pose estimation method brings
about the problem of cumulative error. In this paper, the concept of virtual
bones is proposed to solve such a challenge. The virtual bones are imaginary
bones between non-adjacent joints. They do not exist in reality, but they bring
new loop constraints for the estimation of 3D human joints. The proposed
network in this paper predicts real bones and virtual bones, simultaneously.
The final length of real bones is constrained and learned by the loop
constructed by the predicted real bones and virtual bones. Besides, the motion
constraints of joints in consecutive frames are considered. The consistency
between the 2D projected position displacement predicted by the network and the
captured real 2D displacement by the camera is proposed as a new projection
consistency loss for the learning of 3D human pose. The experiments on the
Human3.6M dataset demonstrate the good performance of the proposed method.
Ablation studies demonstrate the effectiveness of the proposed inter-frame
projection consistency constraints and intra-frame loop constraints.
