# Synthesising Dynamic Textures using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1702.07006 · HTML: https://mlsanity.com/paper/1702.07006/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2017.

## Built on by (most cited first, 6 of 6)

- [DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata](https://mlsanity.com/paper/2211.11417/) (2022)
- [Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations](https://mlsanity.com/paper/2104.05940/) (2021)
- [DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network](https://mlsanity.com/paper/2412.16948/) (2024)
- [Few-shot Shape Recognition by Learning Deep Shape-aware Features](https://mlsanity.com/paper/2312.01315/) (2023)
- [A spatiotemporal style transfer algorithm for dynamic visual stimulus generation](https://mlsanity.com/paper/2403.04940/) (2024)
- [Neural Differential Appearance Equations](https://mlsanity.com/paper/2410.07128/) (2024)

## Abstract (arXiv)

Here we present a parametric model for dynamic textures. The model is based on spatiotemporal summary statistics computed from the feature representations of a Convolutional Neural Network (CNN) trained on object recognition. We demonstrate how the model can be used to synthesise new samples of dynamic textures and to predict motion in simple movies.