  Shape deformation is an important component in any geometry processing
toolbox. The goal is to enable intuitive deformations of single or multiple
shapes or to transfer example deformations to new shapes while preserving the
plausibility of the deformed shape(s). Existing approaches assume access to
point-level or part-level correspondence or establish them in a preprocessing
phase, thus limiting the scope and generality of such approaches. We propose
DeformSyncNet, a new approach that allows consistent and synchronized shape
deformations without requiring explicit correspondence information.
Technically, we achieve this by encoding deformations into a class-specific
idealized latent space while decoding them into an individual, model-specific
linear deformation action space, operating directly in 3D. The underlying
encoding and decoding are performed by specialized (jointly trained) neural
networks. By design, the inductive bias of our networks results in a
deformation space with several desirable properties, such as path invariance
across different deformation pathways, which are then also approximately
preserved in real space. We qualitatively and quantitatively evaluate our
framework against multiple alternative approaches and demonstrate improved
performance.
