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The task of shape abstraction with semantic part consistency is challengingdue to the complex geometries of natural objects. Recent methods learn torepresent an object shape using a set of simple primitives to fit the target.\textcolor{black}{However, in these methods, the primitives used do not alwayscorrespond to real parts or lack geometric flexibility for semanticinterpretation.} In this paper, we investigate salient and efficient primitivedescriptors for accurate shape abstractions, and propose \textit{DeepDeformable Models (DDMs)}. DDM employs global deformations and diffeomorphiclocal deformations. These properties enable DDM to abstract complex objectshapes with significantly fewer primitives that offer broader geometry coverageand finer details. DDM is also capable of learning part-level semanticcorrespondences due to the differentiable and invertible properties of ourprimitive deformation. Moreover, DDM learning formulation is based on dynamicand kinematic modeling, which enables joint regularization of eachsub-transformation during primitive fitting. Extensive experiments on\textit{ShapeNet} demonstrate that DDM outperforms the state-of-the-art interms of reconstruction and part consistency by a notable margin.

Deep Deformable Models: Learning 3D Shape Abstractions with Part Consistency