EVOPOSE: A Recursive Transformer For 3D Human Pose Estimation With
  Kinematic Structure Priors
Yaqi Zhang Yan Lu Bin Liu Zhiwei Zhao Qi Chu Nenghai Yu
Transformer is popular in recent 3D human pose estimation, which utilizes
long-term modeling to lift 2D keypoints into the 3D space. However, current
transformer-based methods do not fully exploit the prior knowledge of the human
skeleton provided by the kinematic structure. In this paper, we propose a novel
transformer-based model EvoPose to introduce the human body prior knowledge for
3D human pose estimation effectively. Specifically, a Structural Priors
Representation (SPR) module represents human priors as structural features
carrying rich body patterns, e.g. joint relationships. The structural features
are interacted with 2D pose sequences and help the model to achieve more
informative spatiotemporal features. Moreover, a Recursive Refinement (RR)
module is applied to refine the 3D pose outputs by utilizing estimated results
and further injects human priors simultaneously. Extensive experiments
demonstrate the effectiveness of EvoPose which achieves a new state of the art
on two most popular benchmarks, Human3.6M and MPI-INF-3DHP.