  The shape of an object is an important characteristic for many vision
problems such as segmentation, detection and tracking. Being independent of
appearance, it is possible to generalize to a large range of objects from only
small amounts of data. However, shapes represented as silhouette images are
challenging to model due to complicated likelihood functions leading to
intractable posteriors. In this paper we present a generative model of shapes
which provides a low dimensional latent encoding which importantly resides on a
smooth manifold with respect to the silhouette images. The proposed model
propagates uncertainty in a principled manner allowing it to learn from small
amounts of data and providing predictions with associated uncertainty. We
provide experiments that show how our proposed model provides favorable
quantitative results compared with the state-of-the-art while simultaneously
providing a representation that resides on a low-dimensional interpretable
manifold.
