  Human Pose Estimation (HPE) aims at retrieving the 3D position of human
joints from images or videos. We show that current 3D HPE methods suffer a lack
of viewpoint equivariance, namely they tend to fail or perform poorly when
dealing with viewpoints unseen at training time. Deep learning methods often
rely on either scale-invariant, translation-invariant, or rotation-invariant
operations, such as max-pooling. However, the adoption of such procedures does
not necessarily improve viewpoint generalization, rather leading to more
data-dependent methods. To tackle this issue, we propose a novel capsule
autoencoder network with fast Variational Bayes capsule routing, named DECA. By
modeling each joint as a capsule entity, combined with the routing algorithm,
our approach can preserve the joints' hierarchical and geometrical structure in
the feature space, independently from the viewpoint. By achieving viewpoint
equivariance, we drastically reduce the network data dependency at training
time, resulting in an improved ability to generalize for unseen viewpoints. In
the experimental validation, we outperform other methods on depth images from
both seen and unseen viewpoints, both top-view, and front-view. In the RGB
domain, the same network gives state-of-the-art results on the challenging
viewpoint transfer task, also establishing a new framework for top-view HPE.
The code can be found at https://github.com/mmlab-cv/DECA.
