  We propose a novel learnable representation for detail-preserving shape
deformation. The goal of our method is to warp a source shape to match the
general structure of a target shape, while preserving the surface details of
the source. Our method extends a traditional cage-based deformation technique,
where the source shape is enclosed by a coarse control mesh termed \emph{cage},
and translations prescribed on the cage vertices are interpolated to any point
on the source mesh via special weight functions. The use of this sparse cage
scaffolding enables preserving surface details regardless of the shape's
intricacy and topology. Our key contribution is a novel neural network
architecture for predicting deformations by controlling the cage. We
incorporate a differentiable cage-based deformation module in our architecture,
and train our network end-to-end. Our method can be trained with common
collections of 3D models in an unsupervised fashion, without any cage-specific
annotations. We demonstrate the utility of our method for synthesizing shape
variations and deformation transfer.
