Human pose estimation (HPE) is an ever-growing research field, with an increasing number of publications in the computer vision and deep learning fields and it covers a multitude of practical scenarios, from sports to entertainment and from surveillance to medical applications. Despite the impressive results that can be obtained with HPE, there are still many problems that need to be tackled when dealing with real-world applications. Most of the issues are linked to a poor or completely wrong detection of the pose that emerges from the inability of the network to model the viewpoint. This thesis shows how designing viewpoint-equivariant neural networks can lead to substantial improvements in the field of human pose estimation, both in terms of state-of-the-art results and better real-world applications. By jointly learning how to build hierarchical human body poses together with the observer viewpoint, a network can learn to generalise its predictions when dealing with previously unseen viewpoints. As a result, the amount of training data needed can be drastically reduced, simultaneously leading to faster and more efficient training and more robust and interpretable real-world applications.