Estimating human pose from images is the key to enabling machines to understand human actions. Existing works on human pose estimation mainly focus on designing more resultful deep neural networks to regress the locations of human joints. Although the human pose is obedient to anatomy and shows rich anatomical features, reasoning human body structure by machine in a complex environment is still an open problem. This chapter proposes AnatPose which can effectively capture the structural dependency among human body parts by both deep neural network architecture and learning objectives: (1) For the deep neural network architecture, a bidirectional learning paradigm is proposed to learn body-part proportions and dependencies by organizing human body parts as sequential data. This innovation enables the messages to pass in a bidirectional way and makes the human body exchange information about each part deeper during training. (2) For the learning objective, the proposed AnatPose learns a probabilistic representation of multi-scale anatomical features, including keypoint heatmaps, bone heatmaps, and symmetry heatmaps. This innovation enables the multi-scale anatomical features to successfully capture the structural dependency at both low-level joints and high-level associations from the anatomical priors of the human body. Extensive experimental results demonstrate that the proposed AnatPose shows state-of-the-art performance on three challenging datasets. It achieves a PCK@0.2 detection rate of 95.2% on the LSP dataset, a PCKh@0.5 detection rate of 92.9% on the MPII dataset, and an mAP of 76.6% on the Microsoft COCO dataset. Benefiting from its state-of-the-art accuracy, the proposed approach is expected to be widely used in various human pose estimation-driven applications.