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For the current 3D human pose estimation task, a group of methods mainlylearn the rules of 2D-3D projection from spatial and temporal correlation.However, earlier methods model the global features of the entire body joint inthe time domain, but ignore the motion trajectory of individual joint. Therecent work [29] considers that there are differences in motion betweendifferent joints and deals with the temporal relationship of each jointseparately. However, we found that different joints show the same movementtrends under some specific actions. Therefore, our proposed Fusionformer methodintroduces a self-trajectory module and a mutual-trajectory module based on thespatio-temporal module .After that, the global spatio-temporal features andlocal joint trajectory features are fused through a linear network in aparallel manner. To eliminate the influence of bad 2D poses on 3D projections,finally we also introduce a pose refinement network to balance the consistencyof 3D projections. In addition, we evaluate the proposed method on twobenchmark datasets (Human3.6M, MPI-INF-3DHP). Comparing our method with thebaseline method poseformer, the results show an improvement of 2.4% MPJPE and4.3% P-MPJPE on the Human3.6M dataset, respectively.

(Fusionformer):Exploiting the Joint Motion Synergy with Fusion Network Based On Transformer for 3D Human Pose Estimation