  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.
