PS-Mamba: Spatial-Temporal Graph Mamba for Pose Sequence Refinement

Human pose sequence refinement plays a crucial role in improving the temporal coherence of pose estimation across the sequence of frames. Despite its importance in realworld applications, human pose sequence refinement has received less attention than human pose estimation. In this paper, we propose PS-Mamba, a novel framework that refines human pose sequences by integrating spatial-temporal graph learning with state space modeling. Specifically, we introduce the Spatial-Temporal Graph State Space (ST-GSS) block, which captures spatial and temporal dependencies across joints to smooth pose sequences while preserving structural integrity. The spatial-temporal graph learns intricate joint interactions, while the state space component effectively manages temporal dynamics, reducing both short-and long-term pose instability. Besides, we incorporate a dynamic graph weight matrix to adaptively model the relative influence of joint interactions, further mitigating pose ambiguity. Experiments on challenging benchmarks show that our PS-Mamba outperforms SOTAs, achieving -14.21 mm MPJPE (+18.5% ↑), -13.59 mm PA-MPJPE (+22.1% ↑), and -0.42 mm/s² ACCEL (+9.7% ↑) compared to SynSP on AIST++, significantly reducing jitters and enhancing pose stability. The code is available at https://github.com/donghaoye/ps-mamba.