Mamba-based methods for video-based 3D human pose estimation can model long sequences with linear complexity, but their selective state updates usually lack direct exploitation of skeletal relations. Meanwhile, stacking modeling blocks at the full temporal resolution leads to high computational overhead, while compressing the temporal sequence makes it difficult to retain per-frame details. To address these issues, we propose SCR-Mamba, whose core consists of Skeleton-Conditioned State Modeling (SCSM) and Cross-Scale Incremental Refinement (CSIR). SCSM generates a skeleton context from bone connections and left-right symmetry relations and generates the selective parameters of the spatial and temporal branches conditioned on it, so that structural relations are involved in the writing, propagation, and reading of state information. CSIR constructs multi-scale motion representations through content-adaptive aggregation and progressively refines the retained per-frame features with the increments produced by coarse-scale modeling, integrating long-range motion context while reducing full-resolution computation. On Human3.6M with Stacked Hourglass (SH) 2D detections and 243-frame inputs, SCR-Mamba-L achieves 36.8 mm MPJPE (P1) with 7.72 M parameters and 15.10 G MACs, less than 1/7 of the computational cost of StructMamPose-L with similar accuracy; SCR-Mamba-X further reaches 36.6 mm P1 and 30.9 mm P-MPJPE (P2), achieving state-of-the-art performance. On MPI-INF-3DHP, SCR-Mamba-X achieves a percentage of correct keypoints (PCK) of 99.3%, an area under the curve (AUC) of 88.1%, and an MPJPE of 14.2 mm.

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