AI 中文总结
本文提出基于MHR姿态的MHRGait步态识别方法，建模关节控制的时空特征，其变体MHRGait++融合轮廓模态，在多数据集取得优异性能，参数与计算量更低，为步态识别提供新表征思路。
AI 中文摘要
步态识别的性能受输入表征形式影响：人体轮廓编码投影后的身体形状，骨骼编码稀疏关节坐标，三维网格编码密集表面几何，各类表征中承载身份信息的关节活动会随衣着、骨骼尺度或体型变化。本文探究能否通过紧凑的关节控制实现步态识别，提出动量人体骨骼（MHR）姿态作为步态表征，利用单目视频估计的184个语义化身体与手部参数描述每帧姿态；MHRGait按解剖结构分组异质控制量，建模帧内协同与时序演化，生成紧凑的身体与手部描述符；进一步提出MHRGait++，通过模态平衡距离融合将MHR姿态与轮廓结合，避免描述符数量决定模态重要性。在四个基准数据集上的实验显示，MHRGait在CCPG和SUSTech1K的对比模型基方法中取得最优整体性能，且跨数据集迁移效果良好，其识别网络处理30帧输入仅需276万参数和0.69 GFLOPs；MHRGait++可持续提升轮廓识别器性能，实现准确率与效率的良好权衡。上述结果表明，骨骼控制空间的关节活动是有效的独立步态表征，也是投影身体形状的互补线索，代码可在指定URL获取。
英文摘要
Gait recognition is shaped by its input representation. Silhouettes encode projected body shape, skeletons encode sparse joint coordinates, and 3D meshes encode dense surface geometry. In each case, identity-bearing articulation is observed through geometric carriers that also vary with clothing, skeletal scale, or body shape. We investigate whether gait can instead be recognized from compact articulated controls. We introduce Momentum Human Rig (MHR) pose as a gait representation, describing each frame using 184 semantically organized body and hand parameters estimated from monocular video. MHRGait groups these heterogeneous controls by anatomy, models their intra-frame coordination and temporal evolution, and produces compact body and hand descriptors. We further introduce MHRGait++, which combines MHR pose with silhouettes through modality-balanced distance fusion, preventing descriptor count from determining modality importance. Experiments on four benchmarks show that MHRGait attains the best overall performance among compared model-based methods on CCPG and SUSTech1K and transfers effectively across datasets, while its recognition network requires only 2.76M parameters and 0.69 GFLOPs for a 30-frame input. MHRGait++ consistently improves silhouette recognizers with a favorable accuracy-efficiency trade-off. These results establish rig-space articulation as an effective standalone gait representation and a complementary cue to projected body shape. Our code is available at https://github.com/duanhuiran/MHRGait.
