  Analyzing and training 3D body posture models depend heavily on the
availability of joint labels that are commonly acquired through laborious
manual annotation of body joints or via marker-based joint localization using
carefully curated markers and capturing systems. However, such annotations are
not always available, especially for people performing unusual activities. In
this paper, we propose an algorithm that learns to discover 3D keypoints on
human bodies from multiple-view images without any supervision or labels other
than the constraints multiple-view geometry provides. To ensure that the
discovered 3D keypoints are meaningful, they are re-projected to each view to
estimate the person's mask that the model itself has initially estimated
without supervision. Our approach discovers more interpretable and accurate 3D
keypoints compared to other state-of-the-art unsupervised approaches on
Human3.6M and MPI-INF-3DHP benchmark datasets.
