关键词: 三维人体姿态估计, 图注意力机制, 时间上下文, 空间上下文, 时间卷积网络

Abstract: According to recent research data on human pose estimation, making full potential of 2D pose sequences to acquire representative characteristics for producing a 3D pose estimation still awaits a definitive answer. A temporal context network with graph attention was developed, incorporating a temporal context network with graph attention, which included Temporal Context Network with Shifted windows (STCN), Extremity-Guided graph global Attention Network (EGAT), and Pose Grammar-based graph Convolution Network (PGCN). Firstly, STCN was explored to transform the 2D joint position of long sequences into a single 3D human pose, which not only effectively aggregated and utilized the long-range and short-range pose information, but also substantially cut down the computational cost. Through research, in 3D pose estimation, spatial feature was essential for solving depth ambiguity and occlusion problems. So far, there was no effective solution to simultaneously capture the changing spatial articulations flexibly and achieve real-time 3D pose estimation efficiently. EGAT module was present for efficiently computing global spatial context. This module treated human body edge nodes as “traffic hubs”, and established bridges for information exchange between them and other nodes. It utilized graph convolution for information propagation and graph attention mechanism for adaptive weight assignment to perform global context computation on human body joints. Finally, PGCN module was designed to utilize Graph Convolution Network (GCN) for computation and modeling local spatial context. The approach combined the imposition of additional temporal smoothness constraints under single-target frame supervision, thereby contributing to the generation of more precise and smoother 3D poses. Comprehensive inferential evaluations of the proposed model were conducted on the two complex benchmark datasets, Human3.6m and Humaneva-I. The results demonstrate that the approach exhibits superior performance. Specifically, when the number of input frames is set to 81, the experimental result of the approach on the Human3.6M dataset achieves a Mean Per Joint Position Error (MPJPE) of 43.5mm. This represents a 9% reduction in prediction error compared to the state-of-the-art SCNet (Spatial Collaboration Network), thereby showcasing enhanced accuracy. Code can be found at https://github.com/zzddwyff/upgraded-octo-carnival.

Key words: 3D human pose estimation, graph attention, temporal context, special context, temporal convolution network