  For the current 3D human pose estimation task, a group of methods mainly
learn the rules of 2D-3D projection from spatial and temporal correlation.
However, earlier methods model the global features of the entire body joint in
the time domain, but ignore the motion trajectory of individual joint. The
recent work [29] considers that there are differences in motion between
different joints and deals with the temporal relationship of each joint
separately. However, we found that different joints show the same movement
trends under some specific actions. Therefore, our proposed Fusionformer method
introduces a self-trajectory module and a mutual-trajectory module based on the
spatio-temporal module .After that, the global spatio-temporal features and
local joint trajectory features are fused through a linear network in a
parallel manner. To eliminate the influence of bad 2D poses on 3D projections,
finally we also introduce a pose refinement network to balance the consistency
of 3D projections. In addition, we evaluate the proposed method on two
benchmark datasets (Human3.6M, MPI-INF-3DHP). Comparing our method with the
baseline method poseformer, the results show an improvement of 2.4% MPJPE and
4.3% P-MPJPE on the Human3.6M dataset, respectively.
