SAGCN: Self-Attention Graph Convolutional Network for Human Pose Embedding

Accurate human pose embedding is crucial for action recognition. While traditional convolutional neural networks (CNNs) have advanced pose feature extraction, they struggle to model structural relationships and long-range dependencies between keypoints, and are less robust to occlusions. To address these limitations, we present SAGCN, a novel model integrating graph convolutional network (GCN) with self-attention. SAGCN leverages GCN to preserve keypoint structure and self-attention to capture long-range dependencies. We further introduce probabilistic pose embeddings to represent inherent multi-view pose uncertainty. Evaluated on Human 3.6M and MPI-INF-3DHP for cross-view retrieval, and on PenAction for sequence alignment, SAGCN outperforms existing methods in retrieval and achieves competitive alignment results compared to specialized approaches.