  We introduce a modeling tool which can evolve a set of 3D objects in a
functionality-aware manner. Our goal is for the evolution to generate large and
diverse sets of plausible 3D objects for data augmentation, constrained
modeling, as well as open-ended exploration to possibly inspire new designs.
Starting with an initial population of 3D objects belonging to one or more
functional categories, we evolve the shapes through part recombination to
produce generations of hybrids or crossbreeds between parents from the
heterogeneous shape collection. Evolutionary selection of offsprings is guided
both by a functional plausibility score derived from functionality analysis of
shapes in the initial population and user preference, as in a design gallery.
Since cross-category hybridization may result in offsprings not belonging to
any of the known functional categories, we develop a means for functionality
partial matching to evaluate functional plausibility on partial shapes. We show
a variety of plausible hybrid shapes generated by our functionality-aware model
evolution, which can complement existing datasets as training data and boost
the performance of contemporary data-driven segmentation schemes, especially in
challenging cases. Our tool supports constrained modeling, allowing users to
restrict or steer the model evolution with functionality labels. At the same
time, unexpected yet functional object prototypes can emerge during open-ended
exploration owing to structure breaking when evolving a heterogeneous
collection.
