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Abstract

We introduce morphable part models for smart shape manipulation using an assembly of deformable parts with appropriate boundary conditions. In an analysis phase, we characterize the continuous allowable variations both for the individual parts and their interconnections using Gaussian shape models with low rank covariance. The discrete aspect of how parts can be assembled is captured using a shape grammar. The parts and their interconnection rules are learned semi-automatically from symmetries within a single object or from semantically corresponding parts across a larger set of example models. The learned discrete and continuous structure is encoded as a graph. In the interaction phase, we obtain an interactive yet intuitive shape deformation framework producing realistic deformations on classes of objects that are difficult to edit using existing structure-aware deformation techniques. Unlike previous techniques, our method uses self-similarities from a single model as training input and allows the user to reassemble the identified parts in new configurations, thus exploiting both the discrete and continuous learned variations while ensuring appropriate boundary conditions across part boundaries.