DeepMLS: Geometry-Aware Control Point Deformation. (arXiv:2201.01873v1 [cs.GR])

We introduce DeepMLS, a space-based deformation technique, guided by a set of
displaced control points. We leverage the power of neural networks to inject
the underlying shape geometry into the deformation parameters. The goal of our
technique is to enable a realistic and intuitive shape deformation. Our method
is built upon moving least-squares (MLS), since it minimizes a weighted sum of
the given control point displacements. Traditionally, the influence of each
control point on every point in space (i.e., the …

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