Synthesising Dynamic Textures using Convolutional Neural Networks
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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.
Key facts. Cited by 6 papers in the map (data through 13 Oct 2026). First posted on arXiv 22 Feb 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 3.
6 papers.
- 2022DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automatacited by 18
- 2021Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlationscited by 4
- 2024DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Networkcited by 3
- 2023Few-shot Shape Recognition by Learning Deep Shape-aware Featurescited by 2
- 2024A spatiotemporal style transfer algorithm for dynamic visual stimulus generationcited by 2
- 2024Neural Differential Appearance Equationscited by 1
- 2024DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network22 Dec 2024
- 2024Neural Differential Appearance Equations23 Sep 2024
- 2024A spatiotemporal style transfer algorithm for dynamic visual stimulus generation7 Mar 2024
- 2023Few-shot Shape Recognition by Learning Deep Shape-aware Features3 Dec 2023
- 2022DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata21 Nov 2022
- 2021Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations13 Apr 2021