Texture synthesis using convolutional neural networks with long-range consistency and spectral constraints

Shaun Schreiber, Jaco Geldenhuys, Hendrik De Villiers

Research output: Chapter in Book/Report/Conference proceedingConference paper

1 Citation (Scopus)

Abstract

Procedural texture generation enables the creation of more rich and detailed virtual environments without the help of an artist. However, finding a flexible generative model of real world textures remains an open problem. We present a novel Convolutional Neural Network based texture model consisting of two summary statistics (the Gramian and Translation Gramian matrices), as well as spectral constraints. We investigate the Fourier Transform or Window Fourier Transform in applying spectral constraints, and find that the Window Fourier Transform improved the quality of the generated textures. We demonstrate the efficacy of our system by comparing generated output with that of related state of the art systems.

Original languageEnglish
Title of host publication2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, PRASA-RobMech 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781509033355
DOIs
Publication statusPublished - 2017
Event2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, PRASA-RobMech 2016 - Stellenbosch, South Africa
Duration: 30 Nov 20162 Dec 2016

Conference

Conference2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, PRASA-RobMech 2016
CountrySouth Africa
CityStellenbosch
Period30/11/162/12/16

Fingerprint Dive into the research topics of 'Texture synthesis using convolutional neural networks with long-range consistency and spectral constraints'. Together they form a unique fingerprint.

  • Cite this

    Schreiber, S., Geldenhuys, J., & De Villiers, H. (2017). Texture synthesis using convolutional neural networks with long-range consistency and spectral constraints. In 2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference, PRASA-RobMech 2016 [7813173] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RoboMech.2016.7813173