Abstract: Human pose sequence refinement plays a crucial role in improving the accuracy, smoothness, and temporal coherence of pose estimation across a sequence of frames. Despite its importance in real-world 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 models intricate joint interactions, while the state space component effectively manages temporal dynamics, reducing both short- and long-term pose instability. Additionally, we incorporate a dynamic graph weight matrix to adaptively model the relative influence of joint interactions, further mitigating pose ambiguity. Extensive experiments on challenging benchmarks demonstrate that our PS-Mamba outperforms SOTAs, achieving $\mathbf{-14.21}$ mm MPJPE (+18.5\%), $\mathbf{-13.59}$ mm PA-MPJPE (+22.1\%), and $\mathbf{-0.42}$ mm/s² ACCEL (+9.7\%) compared to SynSP on AIST++, significantly reducing jitters and enhancing pose stability. Our code has been submitted as supplementary and will be open-sourced upon acceptance.