3D Topology Transformation with Generative Adversarial Networks

TL;DR

This paper introduces Vox2Vox, a GAN-based method for transforming 3D shapes into new topologies while preserving their original structure, enabling creative and customizable 3D design.

Contribution

It presents a novel 3D-to-3D topology transformation technique using a modified pix2pix GAN, expanding creative possibilities for 3D shape generation.

Findings

Vox2Vox effectively transforms 3D models into new topologies.

The method produces novel and inspiring 3D shapes.

Compared to baseline, Vox2Vox shows superior transformation quality.

Abstract

Generation and transformation of images and videos using artificial intelligence have flourished over the past few years. Yet, there are only a few works aiming to produce creative 3D shapes, such as sculptures. Here we show a novel 3D-to-3D topology transformation method using Generative Adversarial Networks (GAN). We use a modified pix2pix GAN, which we call Vox2Vox, to transform the volumetric style of a 3D object while retaining the original object shape. In particular, we show how to transform 3D models into two new volumetric topologies - the 3D Network and the Ghirigoro. We describe how to use our approach to construct customized 3D representations. We believe that the generated 3D shapes are novel and inspirational. Finally, we compare the results between our approach and a baseline algorithm that directly convert the 3D shapes, without using our GAN.