  Camera captured human pose is an outcome of several sources of variation.
Performance of supervised 3D pose estimation approaches comes at the cost of
dispensing with variations, such as shape and appearance, that may be useful
for solving other related tasks. As a result, the learned model not only
inculcates task-bias but also dataset-bias because of its strong reliance on
the annotated samples, which also holds true for weakly-supervised models.
Acknowledging this, we propose a self-supervised learning framework to
disentangle such variations from unlabeled video frames. We leverage the prior
knowledge on human skeleton and poses in the form of a single part-based 2D
puppet model, human pose articulation constraints, and a set of unpaired 3D
poses. Our differentiable formalization, bridging the representation gap
between the 3D pose and spatial part maps, not only facilitates discovery of
interpretable pose disentanglement but also allows us to operate on videos with
diverse camera movements. Qualitative results on unseen in-the-wild datasets
establish our superior generalization across multiple tasks beyond the primary
tasks of 3D pose estimation and part segmentation. Furthermore, we demonstrate
state-of-the-art weakly-supervised 3D pose estimation performance on both
Human3.6M and MPI-INF-3DHP datasets.
