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

PT2PC: Learning to Generate 3D Point Cloud Shapes from Part Tree Conditions