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キーワード:
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要旨:
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.
