# A geometrically aware auto-encoder for multi-texture synthesis

arXiv: https://arxiv.org/abs/2302.01616 · HTML: https://mlsanity.com/paper/2302.01616/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 21 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 18 of 18)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [Universal Style Transfer via Feature Transforms](https://mlsanity.com/paper/1705.08086/) (2017)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images](https://mlsanity.com/paper/1603.03417/) (2016)
- [Learning Texture Manifolds with the Periodic Spatial GAN](https://mlsanity.com/paper/1705.06566/) (2017)
- [Learning a Neural 3D Texture Space from 2D Exemplars](https://mlsanity.com/paper/1912.04158/) (2019)
- [Texture Synthesis with Spatial Generative Adversarial Networks](https://mlsanity.com/paper/1611.08207/) (2016)
- [Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture](https://mlsanity.com/paper/1901.03447/) (2019)
- [Texture Synthesis Using Convolutional Neural Networks](https://mlsanity.com/paper/1505.07376/) (2015)
- [Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter](https://mlsanity.com/paper/2007.07243/) (2020)
- [Second-order Convolutional Neural Networks](https://mlsanity.com/paper/1703.06817/) (2017)
- [High resolution neural texture synthesis with long range constraints](https://mlsanity.com/paper/2008.01808/) (2020)
- [Paying U-Attention to Textures: Multi-Stage Hourglass Vision Transformer for Universal Texture Synthesis](https://mlsanity.com/paper/2202.11703/) (2022)
- [Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2](https://mlsanity.com/paper/2203.04221/) (2022)
- [User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/1904.04751/) (2019)

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

- [Multispectral Texture Synthesis using RGB Convolutional Neural Networks](https://mlsanity.com/paper/2410.16019/) (2024)

## Abstract (arXiv)

We propose an auto-encoder architecture for multi-texture synthesis. The approach relies on both a compact encoder accounting for second order neural statistics and a generator incorporating adaptive periodic content. Images are embedded in a compact and geometrically consistent latent space, where the texture representation and its spatial organisation are disentangled. Texture synthesis and interpolation tasks can be performed directly from these latent codes. Our experiments demonstrate that our model outperforms state-of-the-art feed-forward methods in terms of visual quality and various texture related metrics.