提出结构感知的轻量级模型，更好捕捉人体姿态的时空关系。
SasMamba: A Lightweight Structure-Aware Stride State Space Model for 3D Human Pose Estimation
- 用结构感知卷积动态建模关节间局部关系
- 采用步进扫描策略构建多尺度全局结构表征
- 参数少且性能强，适合实时姿态估计应用
基于状态空间模型（SSM）的Mamba架构因其线性复杂度和强大的全局建模能力，在3D人体姿态估计中受到关注。然而，现有基于SSM的方法通常使用人工设计的扫描操作将检测到的2D姿态序列展平为纯时间序列，无论局部还是全局，都会破坏人体姿态的固有空间结构，并混淆时空特征，难以捕捉复杂的姿态依赖关系。为解决这一问题，我们提出骨架结构感知的步进状态空间模型（SAS-SSM），首先利用结构感知的时空卷积动态捕获关节间的必要局部交互，然后通过基于步进的扫描策略构建多尺度全局结构表征。该方法在保持线性计算复杂度的同时，灵活建模局部与全局姿态信息。基于SAS-SSM构建的SasMamba模型，在参数显著减少的情况下，仍能达到与现有混合模型相当的3D姿态估计性能。
原文摘要 · Abstract (English)
Recently, the Mamba architecture based on State Space Models (SSMs) has gained attention in 3D human pose estimation due to its linear complexity and strong global modeling capability. However, existing SSM-based methods typically apply manually designed scan operations to flatten detected 2D pose sequences into purely temporal sequences, either locally or globally. This approach disrupts the inherent spatial structure of human poses and entangles spatial and temporal features, making it difficult to capture complex pose dependencies. To address these limitations, we propose the Skeleton Structure-Aware Stride SSM (SAS-SSM), which first employs a structure-aware spatiotemporal convolution to dynamically capture essential local interactions between joints, and then applies a stride-based scan strategy to construct multi-scale global structural representations. This enables flexible modeling of both local and global pose information while maintaining linear computational complexity. Built upon SAS-SSM, our model SasMamba achieves competitive 3D pose estimation performance with significantly fewer parameters compared to existing hybrid models. The source code is available at https://hucui2022.github.io/sasmamba_proj/.
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