On Demand Solid Texture Synthesis Using Deep 3D Networks

Abstract : This paper addresses the problem of generating volumetric data for solid texture synthesis. We propose a compact, memory efficient, convolutional neural network (CNN) which is trained from an example image. The features to be reproduced are analyzed from the example using deep CNN filters that are previously learnt from a dataset. After training the generator is capable of synthesizing solid textures of arbitrary sizes controlled by the user. The main advantage of the proposed approach in comparison to previous patch-based methods is that the creation of a new volume of data can be done in interactive rates.
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https://hal.archives-ouvertes.fr/hal-01678122
Contributor : Jorge Alberto Gutierrez Ortega <>
Submitted on : Monday, January 8, 2018 - 9:28:40 PM
Last modification on : Friday, September 20, 2019 - 4:34:03 PM
Long-term archiving on: Wednesday, May 23, 2018 - 3:37:53 PM

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  • HAL Id : hal-01678122, version 1

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Jorge Gutierrez, Julien Rabin, Bruno Galerne, Thomas Hurtut. On Demand Solid Texture Synthesis Using Deep 3D Networks. 2018. ⟨hal-01678122v1⟩

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