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Synthesising Dynamic Textures using Convolutional Neural Networks

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arxiv 1702.07006 v1 pith:332SPPQV submitted 2017-02-22 cs.CV

Christina M. Funke , Leon A. Gatys , Alexander S. Ecker , Matthias Bethge This is my paper · ORCID
classification cs.CV
keywords dynamicmodeltexturesconvolutionalneuralcomputeddemonstratefeature
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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DTSGAN: Learning Dynamic Textures via Spatiotemporal Generative Adversarial Network

    cs.CV 2024-12 reject novelty 4.0 of 10

    DTSGAN adapts SinGAN-style multi-scale generation to video with 3D convolutions and a sliding-window data update, claiming improved dynamic texture synthesis and diversity.

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