关键词: 三维人体姿态估计, 动态融合, 多假设交互机制, 注意力机制, 相对位置编码

Abstract: Accurately predicting ambiguous 3D human poses from monocular videos remains a significant research challenge. While existing methods can predict 3D joint coordinates using deep learning models, most fail to adequately consider the multi-solution nature of this inverse problem. Although some multi-hypothesis prediction methods address ambiguity, they suffer from insufficient cross-level feature fusion. To address these issues, a 3D human pose estimation model was proposed based on a Temporal-Spatial Feature Pyramid Network and a multi-hypothesis interaction mechanism. First, a Transformer encoder was utilized to capture the multi-modal distribution of possible poses using multi-head self-attention, generating multiple initial hypothesis features. Then, a Temporal-Spatial Feature Pyramid Network (TSP-FPN) was designed, employing a gated adaptive fusion strategy to achieve dynamic weighted integration of multi-level skeleton sequence features, effectively balancing the fusion of local details and global temporal information. Additionally, based on relevant algorithms, a multi-hypothesis optimization module was implemented that combined joint relative position encoding with a cross-attention mechanism, facilitating cross-hypothesis communication and feature aggregation to enhance the model's long-range reasoning capability regarding human topology for high-precision 3D joint coordinate predictions. Experimental results on the Human3.6M dataset demonstrate that the proposed model achieved a Mean Per-Joint Position Error (MPJPE) of 42.3 mm, reducing prediction error by 1.6% compared to the state-of-the-art method Multi-Hypothesis Transformer (MHFormer), indicating substantial progress in addressing monocular 3D pose estimation ambiguity.

Key words: 3D human pose estimation, dynamic fusion, multi-hypothesis interaction mechanism, attention mechanism, relative positional encoding