# Incorporating long-range consistency in CNN-based texture generation

arXiv: https://arxiv.org/abs/1606.01286 · HTML: https://mlsanity.com/paper/1606.01286/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2016.

## Built on by (most cited first, 8 of 8)

- [FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation](https://mlsanity.com/paper/2204.01587/) (2022)
- [Neural Neighbor Style Transfer](https://mlsanity.com/paper/2203.13215/) (2022)
- [Deep Translation Prior: Test-time Training for Photorealistic Style Transfer](https://mlsanity.com/paper/2112.06150/) (2021)
- [Generalized Rectifier Wavelet Covariance Models For Texture Synthesis](https://mlsanity.com/paper/2203.07902/) (2022)
- [Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations](https://mlsanity.com/paper/2104.05940/) (2021)
- [CAMS: Color-Aware Multi-Style Transfer](https://mlsanity.com/paper/2106.13920/) (2021)
- [Perceptual misalignment of texture representations in convolutional neural networks](https://mlsanity.com/paper/2604.01341/) (2026)
- [Neural Artistic Style and Color Transfer Using Deep Learning](https://mlsanity.com/paper/2508.08608/) (2025)

## Abstract (arXiv)

Gatys et al. (2015) showed that pair-wise products of features in a convolutional network are a very effective representation of image textures. We propose a simple modification to that representation which makes it possible to incorporate long-range structure into image generation, and to render images that satisfy various symmetry constraints. We show how this can greatly improve rendering of regular textures and of images that contain other kinds of symmetric structure. We also present applications to inpainting and season transfer.