  3D human pose estimation is a challenging task because of the difficulty to
acquire ground-truth data outside of controlled environments. A number of
further issues have been hindering progress in building a universal and robust
model for this task, including domain gaps between different datasets, unseen
actions between train and test datasets, various hardware settings and high
cost of annotation, etc. In this paper, we propose an algorithm to generate
infinite 3D synthetic human poses (Legatus) from a 3D pose distribution based
on 10 initial handcrafted 3D poses (Decanus) during the training of a 2D to 3D
human pose lifter neural network. Our results show that we can achieve 3D pose
estimation performance comparable to methods using real data from specialized
datasets but in a zero-shot setup, showing the generalization potential of our
framework.
