  The analysis of deforming 3D surface meshes is accelerated by autoencoders
since the low-dimensional embeddings can be used to visualize underlying
dynamics. But, state-of-the-art mesh convolutional autoencoders require a fixed
connectivity of all input meshes handled by the autoencoder. This is due to
either the use of spectral convolutional layers or mesh dependent pooling
operations. Therefore, the types of datasets that one can study are limited and
the learned knowledge cannot be transferred to other datasets that exhibit
similar behavior. To address this, we transform the discretization of the
surfaces to semi-regular meshes that have a locally regular connectivity and
whose meshing is hierarchical. This allows us to apply the same spatial
convolutional filters to the local neighborhoods and to define a pooling
operator that can be applied to every semi-regular mesh. We apply the same mesh
autoencoder to different datasets and our reconstruction error is more than 50%
lower than the error from state-of-the-art models, which have to be trained for
every mesh separately. Additionally, we visualize the underlying dynamics of
unseen mesh sequences with an autoencoder trained on different classes of
meshes.
