# Feature Disentanglement in generating three-dimensional structure from two-dimensional slice with sliceGAN

arXiv: https://arxiv.org/abs/2105.00194 · HTML: https://mlsanity.com/paper/2105.00194/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 1 May 2021.

## Builds on (most cited first, 6 of 6)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Generating 3D structures from a 2D slice with GAN-based dimensionality expansion](https://mlsanity.com/paper/2102.07708/) (2021)
- [Continuous Conversion of CT Kernel using Switchable CycleGAN with AdaIN](https://mlsanity.com/paper/2011.13150/) (2020)

## Built on by (most cited first, 2 of 2)

- [Unsupervised Deep Learning Methods for Biological Image Reconstruction and Enhancement](https://mlsanity.com/paper/2105.08040/) (2021)
- [Generating Heterogeneous 3D Geological Microstructures from 2D Images via a Stable Diffusion-Adversarial Model](https://mlsanity.com/paper/2609.20358/) (2026)

## Abstract (arXiv)

Deep generative models are known to be able to model arbitrary probability distributions. Among these, a recent deep generative model, dubbed sliceGAN, proposed a new way of using the generative adversarial network (GAN) to capture the micro-structural characteristics of a two-dimensional (2D) slice and generate three-dimensional (3D) volumes with similar properties. While 3D micrographs are largely beneficial in simulating diverse material behavior, they are often much harder to obtain than their 2D counterparts. Hence, sliceGAN opens up many interesting directions of research by learning the representative distribution from 2D slices, and transferring the learned knowledge to generate arbitrary 3D volumes. However, one limitation of sliceGAN is that latent space steering is not possible. Hence, we combine sliceGAN with AdaIN to endow the model with the ability to disentangle the features and control the synthesis.