机器人2026，Vol.48Issue(1)：55-65,11.DOI:10.13973/j.cnki.robot.240196
融合双层语义信息的3维人体姿态估计网络
Fusion of Dual-layer Semantic Information for 3D Human Pose Estimation Network
摘要
Abstract
When fusing multiple feasible solutions of human pose,existing methods do not adequately learn the dependen-cies between hypotheses,which easily leads to poor accuracy of the fusion results.Therefore,a 3D human pose estimation network fusing dual-layer semantic information is proposed.A hierarchical feature extraction module is proposed firstly to model the intrinsic structural information of human joint points,extract hypothetical features containing different levels of semantic information,and improve the utilization rate of position information of joint points.In order to further improve the network performance,a feature refinement module is designed secondly to transfer self-information of the hypothetical features,thus enhancing the correlation between joint positions.Finally,a hierarchical feature fusion module and an as-sociation calculation sub-module are proposed to learn the dependency relationship between multi-hypothesis features,and according to the relationship,cross-hypothesis information transfer is carried out between hypothesis features to fuse them into accurate and unified hypothesis features.Therefore,the different levels of semantic information of different hypotheses are fully utilized to obtain the final 3D human pose estimation results.The performance of the proposed model is verified on the Human3.6M,MPI-INF-3DHP and HumanEva-I datasets respectively,and the experimental results show that the proposed method can improve the accuracy of 3D human pose estimation,and effectively deal with the cases of human self-occlusion and complex poses.关键词
3维人体姿态估计/多假设融合/层次特征提取/特征细化Key words
3D human pose estimation/multi-hypothesis fusion/hierarchical feature extraction/feature refinement引用本文复制引用
贾迪,杨柳,徐驰,何德堃..融合双层语义信息的3维人体姿态估计网络[J].机器人,2026,48(1):55-65,11.基金项目
国家自然科学基金(61601213) （61601213）
辽宁省教育厅重点项目(LJ212410147003). （LJ212410147003）
