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We present SP-GAN, a new unsupervised sphere-guided generative model fordirect synthesis of 3D shapes in the form of point clouds. Compared withexisting models, SP-GAN is able to synthesize diverse and high-quality shapeswith fine details and promote controllability for part-aware shape generationand manipulation, yet trainable without any parts annotations. In SP-GAN, weincorporate a global prior (uniform points on a sphere) to spatially guide thegenerative process and attach a local prior (a random latent code) to eachsphere point to provide local details. The key insight in our design is todisentangle the complex 3D shape generation task into a global shape modelingand a local structure adjustment, to ease the learning process and enhance theshape generation quality. Also, our model forms an implicit densecorrespondence between the sphere points and points in every generated shape,enabling various forms of structure-aware shape manipulations such as partediting, part-wise shape interpolation, and multi-shape part composition, etc.,beyond the existing generative models. Experimental results, which include bothvisual and quantitative evaluations, demonstrate that our model is able tosynthesize diverse point clouds with fine details and less noise, as comparedwith the state-of-the-art models.

SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation