# Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

arXiv: https://arxiv.org/abs/1603.03417 · HTML: https://mlsanity.com/paper/1603.03417/

## Key facts

- Cited by 174 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2016.

## Built on by (most cited first, 30 of 174)

- [HINet: Half Instance Normalization Network for Image Restoration](https://mlsanity.com/paper/2105.06086/) (2021)
- [Few-shot Image Generation via Cross-domain Correspondence](https://mlsanity.com/paper/2104.06820/) (2021)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows](https://mlsanity.com/paper/2103.16877/) (2021)
- [Stylizing 3D Scene via Implicit Representation and HyperNetwork](https://mlsanity.com/paper/2105.13016/) (2021)
- [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022)
- [Learning to Stylize Novel Views](https://mlsanity.com/paper/2105.13509/) (2021)
- [Region-aware Adaptive Instance Normalization for Image Harmonization](https://mlsanity.com/paper/2106.02853/) (2021)
- [Unified Implicit Neural Stylization](https://mlsanity.com/paper/2204.01943/) (2022)
- [MagicMix: Semantic Mixing with Diffusion Models](https://mlsanity.com/paper/2210.16056/) (2022)
- [StyleShot: A Snapshot on Any Style](https://mlsanity.com/paper/2407.01414/) (2024)
- [StyleMesh: Style Transfer for Indoor 3D Scene Reconstructions](https://mlsanity.com/paper/2112.01530/) (2021)
- [ArtFID: Quantitative Evaluation of Neural Style Transfer](https://mlsanity.com/paper/2207.12280/) (2022)
- [Rethinking and Improving the Robustness of Image Style Transfer](https://mlsanity.com/paper/2104.05623/) (2021)
- [CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer](https://mlsanity.com/paper/2207.04808/) (2022)
- [3D Photo Stylization: Learning to Generate Stylized Novel Views from a Single Image](https://mlsanity.com/paper/2112.00169/) (2021)
- [UV Volumes for Real-time Rendering of Editable Free-view Human Performance](https://mlsanity.com/paper/2203.14402/) (2022)
- [Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes](https://mlsanity.com/paper/2103.17185/) (2021)
- [Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer](https://mlsanity.com/paper/2104.05376/) (2021)
- [Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer](https://mlsanity.com/paper/2210.00912/) (2022)
- [Generative Modelling of BRDF Textures from Flash Images](https://mlsanity.com/paper/2102.11861/) (2021)
- [ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity](https://mlsanity.com/paper/2103.09776/) (2021)
- [FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation](https://mlsanity.com/paper/2204.01587/) (2022)
- [General Image-to-Image Translation with One-Shot Image Guidance](https://mlsanity.com/paper/2307.14352/) (2023)
- [Style-Aware Normalized Loss for Improving Arbitrary Style Transfer](https://mlsanity.com/paper/2104.10064/) (2021)
- [Generative Human Motion Stylization in Latent Space](https://mlsanity.com/paper/2401.13505/) (2024)
- [Where and What? Examining Interpretable Disentangled Representations](https://mlsanity.com/paper/2104.05622/) (2021)
- [In the light of feature distributions: moment matching for Neural Style Transfer](https://mlsanity.com/paper/2103.07208/) (2021)
- [DreamSpace: Dreaming Your Room Space with Text-Driven Panoramic Texture Propagation](https://mlsanity.com/paper/2310.13119/) (2023)
- [CAP-VSTNet: Content Affinity Preserved Versatile Style Transfer](https://mlsanity.com/paper/2303.17867/) (2023)

## Abstract (arXiv)

Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and memory-consuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains compact feed-forward convolutional networks to generate multiple samples of the same texture of arbitrary size and to transfer artistic style from a given image to any other image. The resulting networks are remarkably light-weight and can generate textures of quality comparable to Gatys~et~al., but hundreds of times faster. More generally, our approach highlights the power and flexibility of generative feed-forward models trained with complex and expressive loss functions.