Bayesian capsule networks for 3D human pose estimation from single 2D images

Abstract

Deep Bayesian Networks are a hot topic in Deep Learning because this approach makes it possible to minimize both the epistemic and the homoscedastic uncertainty at the same time self balancing multiple and complementary losses for a given task, simply by employing standard operations such as dropout, mean squared error or cross-entropy. On the other hand, Capsule networks are a novel DNN architecture that offer a richer representation because each concept is represented by a number of different vectors. The bayesian formulation of the Capsule networks is still an open problem that we address in this paper. We present a bayesian formulation of Capsule networks and compare its performance against the state-of-the-art for the ill-posed regression problem of estimating the 3D human pose from a single 2D image. The results show that our network is very competitive with a much more straightforward solution. To enable fair comparisons the source code is openly available at https://github.com/rollervan/BCN_3DPose/