Rank3DGAN: Semantic mesh generation using relative attributes
This work addresses the need for user-controlled 3D shape generation in domains like computer graphics and design, representing an incremental extension of existing GAN architectures to conditional settings.
The paper tackles the problem of generating 3D shapes conditioned on subjective semantic attributes using generative adversarial networks, achieving a model that learns a controlled latent space and ranking function from pairwise comparisons, as demonstrated on datasets like HumanShape and Basel Face Model.
In this paper, we investigate a novel problem of using generative adversarial networks in the task of 3D shape generation according to semantic attributes. Recent works map 3D shapes into 2D parameter domain, which enables training Generative Adversarial Networks (GANs) for 3D shape generation task. We extend these architectures to the conditional setting, where we generate 3D shapes with respect to subjective attributes defined by the user. Given pairwise comparisons of 3D shapes, our model performs two tasks: it learns a generative model with a controlled latent space, and a ranking function for the 3D shapes based on their multi-chart representation in 2D. The capability of the model is demonstrated with experiments on HumanShape, Basel Face Model and reconstructed 3D CUB datasets. We also present various applications that benefit from our model, such as multi-attribute exploration, mesh editing, and mesh attribute transfer.