  Exploiting the deep generative model's remarkable ability of learning the
data-manifold structure, some recent researches proposed a geometric data
interpolation method based on the geodesic curves on the learned data-manifold.
However, this interpolation method often gives poor results due to a
topological difference between the model and the dataset. The model defines a
family of simply-connected manifolds, whereas the dataset generally contains
disconnected regions or holes that make them non-simply-connected. To
compensate this difference, we propose a novel density regularizer that make
the interpolation path circumvent the holes denoted by low probability density.
We confirm that our method gives consistently better interpolation results from
the experiments with real-world image datasets.
