# Diversified Texture Synthesis with Feed-forward Networks

arXiv: https://arxiv.org/abs/1703.01664 · HTML: https://mlsanity.com/paper/1703.01664/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2017.

## Built on by (most cited first, 30 of 59)

- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [ARF: Artistic Radiance Fields](https://mlsanity.com/paper/2206.06360/) (2022)
- [Stylizing 3D Scene via Implicit Representation and HyperNetwork](https://mlsanity.com/paper/2105.13016/) (2021)
- [Learning to Stylize Novel Views](https://mlsanity.com/paper/2105.13509/) (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)
- [Neural Preset for Color Style Transfer](https://mlsanity.com/paper/2303.13511/) (2023)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [Style-Aware Normalized Loss for Improving Arbitrary Style Transfer](https://mlsanity.com/paper/2104.10064/) (2021)
- [Learning Graph Neural Networks for Image Style Transfer](https://mlsanity.com/paper/2207.11681/) (2022)
- [In the light of feature distributions: moment matching for Neural Style Transfer](https://mlsanity.com/paper/2103.07208/) (2021)
- [AesUST: Towards Aesthetic-Enhanced Universal Style Transfer](https://mlsanity.com/paper/2208.13016/) (2022)
- [Robust Differentiable SVD](https://mlsanity.com/paper/2104.03821/) (2021)
- [QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity](https://mlsanity.com/paper/2212.10431/) (2022)
- [Plan2Scene: Converting Floorplans to 3D Scenes](https://mlsanity.com/paper/2106.05375/) (2021)
- [All-to-key Attention for Arbitrary Style Transfer](https://mlsanity.com/paper/2212.04105/) (2022)
- [Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization](https://mlsanity.com/paper/2103.11784/) (2021)
- [μNCA: Texture Generation with Ultra-Compact Neural Cellular Automata](https://mlsanity.com/paper/2111.13545/) (2021)
- [Consistent Style Transfer](https://mlsanity.com/paper/2201.02233/) (2022)
- [DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer](https://mlsanity.com/paper/2101.06381/) (2021)
- [Fine-Grained Image Style Transfer with Visual Transformers](https://mlsanity.com/paper/2210.05176/) (2022)
- [StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model](https://mlsanity.com/paper/2303.09268/) (2023)
- [Multiple Style Transfer via Variational AutoEncoder](https://mlsanity.com/paper/2110.07375/) (2021)
- [Video Color Grading via Look-Up Table Generation](https://mlsanity.com/paper/2508.00548/) (2025)
- [TGHop: An Explainable, Efficient and Lightweight Method for Texture Generation](https://mlsanity.com/paper/2107.04020/) (2021)
- [Controllable Style Transfer via Test-time Training of Implicit Neural Representation](https://mlsanity.com/paper/2210.07762/) (2022)
- [Passing Multi-Channel Material Textures to a 3-Channel Loss](https://mlsanity.com/paper/2105.13012/) (2021)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)
- [Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric](https://mlsanity.com/paper/2208.00623/) (2022)
- [Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removal](https://mlsanity.com/paper/2104.09457/) (2021)

## Abstract (arXiv)

Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward based methods trade off generality for efficiency, which suffer from many issues, such as shortage of generality (i.e., build one network per texture), lack of diversity (i.e., always produce visually identical output) and suboptimality (i.e., generate less satisfying visual effects). In this work, we focus on solving these issues for improved texture synthesis. We propose a deep generative feed-forward network which enables efficient synthesis of multiple textures within one single network and meaningful interpolation between them. Meanwhile, a suite of important techniques are introduced to achieve better convergence and diversity. With extensive experiments, we demonstrate the effectiveness of the proposed model and techniques for synthesizing a large number of textures and show its applications with the stylization.