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We propose a novel learnable representation for detail-preserving shapedeformation. The goal of our method is to warp a source shape to match thegeneral structure of a target shape, while preserving the surface details ofthe 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 pointon the source mesh via special weight functions. The use of this sparse cagescaffolding enables preserving surface details regardless of the shape'sintricacy and topology. Our key contribution is a novel neural networkarchitecture for predicting deformations by controlling the cage. Weincorporate a differentiable cage-based deformation module in our architecture,and train our network end-to-end. Our method can be trained with commoncollections of 3D models in an unsupervised fashion, without any cage-specificannotations. We demonstrate the utility of our method for synthesizing shapevariations and deformation transfer.

Neural Cages for Detail-Preserving 3D Deformations