  We present MRGAN, a multi-rooted adversarial network which generates
part-disentangled 3D point-cloud shapes without part-based shape supervision.
The network fuses multiple branches of tree-structured graph convolution layers
which produce point clouds, with learnable constant inputs at the tree roots.
Each branch learns to grow a different shape part, offering control over the
shape generation at the part level. Our network encourages disentangled
generation of semantic parts via two key ingredients: a root-mixing training
strategy which helps decorrelate the different branches to facilitate
disentanglement, and a set of loss terms designed with part disentanglement and
shape semantics in mind. Of these, a novel convexity loss incentivizes the
generation of parts that are more convex, as semantic parts tend to be. In
addition, a root-dropping loss further ensures that each root seeds a single
part, preventing the degeneration or over-growth of the point-producing
branches. We evaluate the performance of our network on a number of 3D shape
classes, and offer qualitative and quantitative comparisons to previous works
and baseline approaches. We demonstrate the controllability offered by our
part-disentangled generation through two applications for shape modeling: part
mixing and individual part variation, without receiving segmented shapes as
input.
