Abstract
For the current 3D human pose estimation task, in order to improve the efficiency of pose sequence output, we try to further improve the prediction stability in low input video frame scenarios.Many previous methods lack the understanding of local joint information.\cite{9878888}considers the temporal relationship of a single joint in this work.However, we found that there is a certain predictive correlation between the trajectories of different joints in time.Therefore, our proposed \textbf{Fusionformer} method introduces a self-trajectory module and a cross-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 this http URL eliminate the influence of bad 2D poses on 3D projections, finally we also introduce a pose refinement network to balance the consistency of 3D this http URL 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.
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URL
https://arxiv.org/abs/2210.04006
