  3D generative shape modeling is a fundamental research area in computer
vision and interactive computer graphics, with many real-world applications.
This paper investigates the novel problem of generating 3D shape point cloud
geometry from a symbolic part tree representation. In order to learn such a
conditional shape generation procedure in an end-to-end fashion, we propose a
conditional GAN "part tree"-to-"point cloud" model (PT2PC) that disentangles
the structural and geometric factors. The proposed model incorporates the part
tree condition into the architecture design by passing messages top-down and
bottom-up along the part tree hierarchy. Experimental results and user study
demonstrate the strengths of our method in generating perceptually plausible
and diverse 3D point clouds, given the part tree condition. We also propose a
novel structural measure for evaluating if the generated shape point clouds
satisfy the part tree conditions.
