  Point clouds, as a form of Lagrangian representation, allow for powerful and
flexible applications in a large number of computational disciplines. We
propose a novel deep-learning method to learn stable and temporally coherent
feature spaces for points clouds that change over time. We identify a set of
inherent problems with these approaches: without knowledge of the time
dimension, the inferred solutions can exhibit strong flickering, and easy
solutions to suppress this flickering can result in undesirable local minima
that manifest themselves as halo structures. We propose a novel temporal loss
function that takes into account higher time derivatives of the point
positions, and encourages mingling, i.e., to prevent the aforementioned halos.
We combine these techniques in a super-resolution method with a truncation
approach to flexibly adapt the size of the generated positions. We show that
our method works for large, deforming point sets from different sources to
demonstrate the flexibility of our approach.
