# Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations

arXiv: https://arxiv.org/abs/2104.05940 · HTML: https://mlsanity.com/paper/2104.05940/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Non-Stationary Texture Synthesis by Adversarial Expansion](https://mlsanity.com/paper/1805.04487/) (2018)
- [Learning Dynamic Generator Model by Alternating Back-Propagation Through Time](https://mlsanity.com/paper/1812.10587/) (2018)
- [Two-Stream Convolutional Networks for Dynamic Texture Synthesis](https://mlsanity.com/paper/1706.06982/) (2017)
- [Incorporating long-range consistency in CNN-based texture generation](https://mlsanity.com/paper/1606.01286/) (2016)
- [Synthesising Dynamic Textures using Convolutional Neural Networks](https://mlsanity.com/paper/1702.07006/) (2017)

## Built on by (most cited first, 4 of 4)

- [DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata](https://mlsanity.com/paper/2211.11417/) (2022)
- [AnomalyHop: An SSL-based Image Anomaly Localization Method](https://mlsanity.com/paper/2105.03797/) (2021)
- [PEDENet: Image Anomaly Localization via Patch Embedding and Density Estimation](https://mlsanity.com/paper/2110.15525/) (2021)
- [Neural Differential Appearance Equations](https://mlsanity.com/paper/2410.07128/) (2024)

## Abstract (arXiv)

The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos. The major drawback of existing dynamic texture synthesis models comes from poor treatment of the long-range texture correlation and motion information. To address this problem, we incorporate a new loss term, called the Shifted Gram loss, to capture the structural and long-range correlation of the reference texture video. Furthermore, we introduce a frame sampling strategy to exploit long-period motion across multiple frames. With these two new techniques, the application scope of existing texture synthesis models can be extended. That is, they can synthesize not only homogeneous but also structured dynamic texture patterns. Thorough experimental results are provided to demonstrate that our proposed dynamic texture synthesis model offers state-of-the-art visual performance.