  Statistical shape modeling aims at capturing shape variations of an
anatomical structure that occur within a given population. Shape models are
employed in many tasks, such as shape reconstruction and image segmentation,
but also shape generation and classification. Existing shape priors either
require dense correspondence between training examples or lack robustness and
topological guarantees. We present FlowSSM, a novel shape modeling approach
that learns shape variability without requiring dense correspondence between
training instances. It relies on a hierarchy of continuous deformation flows,
which are parametrized by a neural network. Our model outperforms
state-of-the-art methods in providing an expressive and robust shape prior for
distal femur and liver. We show that the emerging latent representation is
discriminative by separating healthy from pathological shapes. Ultimately, we
demonstrate its effectiveness on two shape reconstruction tasks from partial
data. Our source code is publicly available
(https://github.com/davecasp/flowssm).
