Progressive Multi-View Fusion for 3D Human Pose Estimation

In multi-view 3D human pose estimation (HPE), viewpoint images have large variability due to factors like camera angles and occlusion, making feature extraction and fusion across viewpoints challenging. To address these concerns, we propose a progressive multi-view 3D HPE transformer framework, which achieves effective intra-view pose feature extraction and cross-view fusion by embedding various multi-view fusion methods in the feature extraction process. In order to fully extract spatial semantic features of human joints, we first construct a cross-view spatial fusion module performing spatial feature fusion across adjacent views while mining useful spatial knowledge. To enhance the pose features and alleviate the depth ambiguity problem, we further develop a multi-view spatial-temporal fusion module to extract effective temporal contextual information within the viewpoint and fuse spatial-temporal features across multiple viewpoints. Extensive experiments on two popular 3D HPE benchmarks validate the efficacy and superiority of our method. It outperforms several state-of-the-art methods, effectively alleviates depth ambiguity, and improves 3D pose accuracy without providing camera parameters or complex loss functions.