Resumen
Estimating the positions of human joints from monocular single RGB images has been a challenging task in recent years. Despite great progress in human pose estimation with convolutional neural networks (CNNs), a central problem still exists: the relationships and constraints, such as symmetric relations of human structures, are not well exploited in previous CNN-based methods. Considering the effectiveness of combining local and nonlocal consistencies, we propose an end-to-end self-attention network (SAN) to alleviate this issue. In SANs, attention-driven and long-range dependency modeling are adopted between joints to compensate for local content and mine details from all feature locations. To enable an SAN for both 2D and 3D pose estimations, we also design a compatible, effective and general joint learning framework to mix up the usage of different dimension data. We evaluate the proposed network on challenging benchmark datasets. The experimental results show that our method has significantly achieved competitive results on Human3.6M, MPII and COCO datasets.