# User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1904.04751 · HTML: https://mlsanity.com/paper/1904.04751/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [A geometrically aware auto-encoder for multi-texture synthesis](https://mlsanity.com/paper/2302.01616/) (2023)

## Abstract (arXiv)

We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset. To ensure a dataset coverage, we use an adversarial loss function that penalizes for incorrect reproductions of a given texture. In experiments, we show that our model can learn descriptive texture manifolds for large datasets and from raw data such as a collection of high-resolution photos. Moreover, we apply our method to produce 3D textures and show that it outperforms existing baselines.