  As 3D human pose estimation can now be achieved with very high accuracy in
the supervised learning scenario, tackling the case where 3D pose annotations
are not available has received increasing attention. In particular, several
methods have proposed to learn image representations in a self-supervised
fashion so as to disentangle the appearance information from the pose one. The
methods then only need a small amount of supervised data to train a pose
regressor using the pose-related latent vector as input, as it should be free
of appearance information. In this paper, we carry out in-depth analysis to
understand to what degree the state-of-the-art disentangled representation
learning methods truly separate the appearance information from the pose one.
First, we study disentanglement from the perspective of the self-supervised
network, via diverse image synthesis experiments. Second, we investigate
disentanglement with respect to the 3D pose regressor following an adversarial
attack perspective. Specifically, we design an adversarial strategy focusing on
generating natural appearance changes of the subject, and against which we
could expect a disentangled network to be robust. Altogether, our analyses show
that disentanglement in the three state-of-the-art disentangled representation
learning frameworks if far from complete, and that their pose codes contain
significant appearance information. We believe that our approach provides a
valuable testbed to evaluate the degree of disentanglement of pose from
appearance in self-supervised 3D human pose estimation.

Computer Vision and Pattern Recognition