  We present a generative model to synthesize 3D shapes as sets of handles --
lightweight proxies that approximate the original 3D shape -- for applications
in interactive editing, shape parsing, and building compact 3D representations.
Our model can generate handle sets with varying cardinality and different types
of handles (Figure 1). Key to our approach is a deep architecture that predicts
both the parameters and existence of shape handles, and a novel similarity
measure that can easily accommodate different types of handles, such as cuboids
or sphere-meshes. We leverage the recent advances in semantic 3D annotation as
well as automatic shape summarizing techniques to supervise our approach. We
show that the resulting shape representations are intuitive and achieve
superior quality than previous state-of-the-art. Finally, we demonstrate how
our method can be used in applications such as interactive shape editing,
completion, and interpolation, leveraging the latent space learned by our model
to guide these tasks. Project page: http://mgadelha.me/shapehandles.
