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In this paper, we investigate a novel problem of using generative adversarialnetworks in the task of 3D shape generation according to semantic attributes.Recent works map 3D shapes into 2D parameter domain, which enables trainingGenerative Adversarial Networks (GANs) for 3D shape generation task. We extendthese architectures to the conditional setting, where we generate 3D shapeswith respect to subjective attributes defined by the user. Given pairwisecomparisons of 3D shapes, our model performs two tasks: it learns a generativemodel with a controlled latent space, and a ranking function for the 3D shapesbased on their multi-chart representation in 2D. The capability of the model isdemonstrated with experiments on HumanShape, Basel Face Model and reconstructed3D CUB datasets. We also present various applications that benefit from ourmodel, such as multi-attribute exploration, mesh editing, and mesh attributetransfer.

Rank3DGAN: Semantic mesh generation using relative attributes