用双路网络提升3D人体姿态重建精度，兼顾局部结构与全局时序建模。
HGMamba: Enhancing 3D Human Pose Estimation with a HyperGCN-Mamba Network
- 设计超图与状态空间模型并行处理，分别捕捉关节局部依赖和全局时序关系。
- 在Human3.6M和MPI-INF-3DHP上分别取得38.65mm和14.33mm的P1误差，达领先水平。
- 提供多版本模型，适合不同速度与精度需求的应用场景。
3D人体姿态提升是利用估计或真实2D人体姿态数据进行训练的有前景研究方向。现有方法主要提升估计2D姿态性能，但在真实2D数据上表现不佳。我们发现，从真实2D姿态重建准确3D姿态需精确建模局部姿态结构，并提取鲁棒的全局时空特征。为此，提出新型超图-混洗Mamba（HGMamba）模块，通过两条并行流处理输入：超图（Hyper-GCN）流将人体结构建模为多粒度超图，有效捕捉局部关节依赖；混洗Mamba（Shuffle-Mamba）流利用状态空间模型对所有关节进行时空扫描，建立全局依赖。通过自适应融合两种表示，HGMamba在保持强全局特征建模能力的同时，显著提升局部结构建模效果。堆叠多个HGMamba模块构建三种模型变体，用户可根据速度-精度权衡选择合适配置。在Human3.6M和MPI-INF-3DHP基准数据集上的大量评估表明本方法有效性。HGMamba-B在两个数据集上分别达到38.65mm和14.33mm的P1误差，达到当前最优水平。代码与模型已公开：https://github.com/HuCui2022/HGMamba
原文摘要 · Abstract (English)
3D human pose lifting is a promising research area that leverages estimated and ground-truth 2D human pose data for training. While existing approaches primarily aim to enhance the performance of estimated 2D poses, they often struggle when applied to ground-truth 2D pose data. We observe that achieving accurate 3D pose reconstruction from ground-truth 2D poses requires precise modeling of local pose structures, alongside the ability to extract robust global spatio-temporal features. To address these challenges, we propose a novel Hyper-GCN and Shuffle Mamba (HGMamba) block, which processes input data through two parallel streams: Hyper-GCN and Shuffle-Mamba. The Hyper-GCN stream models the human body structure as hypergraphs with varying levels of granularity to effectively capture local joint dependencies. Meanwhile, the Shuffle Mamba stream leverages a state space model to perform spatio-temporal scanning across all joints, enabling the establishment of global dependencies. By adaptively fusing these two representations, HGMamba achieves strong global feature modeling while excelling at local structure modeling. We stack multiple HGMamba blocks to create three variants of our model, allowing users to select the most suitable configuration based on the desired speed-accuracy trade-off. Extensive evaluations on the Human3.6M and MPI-INF-3DHP benchmark datasets demonstrate the effectiveness of our approach. HGMamba-B achieves state-of-the-art results, with P1 errors of 38.65 mm and 14.33 mm on the respective datasets. Code and models are available: https://github.com/HuCui2022/HGMamba
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