# Rank3DGAN: Semantic mesh generation using relative attributes

## TL;DR

Rank3DGAN introduces a novel method for generating 3D shapes conditioned on subjective semantic attributes using a GAN framework that incorporates pairwise comparisons and ranking functions.

## Contribution

It extends GAN architectures to include conditional generation based on relative attributes and demonstrates applications in mesh editing and attribute transfer.

## Key findings

- Successfully generates 3D shapes with user-defined attributes.
- Learns a ranking function for 3D shapes based on pairwise comparisons.
- Demonstrates applications in multi-attribute exploration and mesh editing.

## Abstract

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.