Learning a Neural 3D Texture Space from 2D Exemplars

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Learning a Neural 3D Texture Space from 2D Exemplars

Henzler, P; Mitra, NJ; Ritschel, T; (2020) Learning a Neural 3D Texture Space from 2D Exemplars. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (pp. pp. 8353-8361). IEEE: Seattle, WA, USA. Green open access

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Abstract

We suggest a generative model of 2D and 3D natural textures with diversity, visual fidelity and at high computational efficiency. This is enabled by a family of methods that extend ideas from classic stochastic procedural texturing (Perlin noise) to learned, deep, non-linearities. Our model encodes all exemplars from a diverse set of textures without a need to be re-trained for each exemplar. Applications include texture interpolation, and learning 3D textures from 2D exemplars.

Type:	Proceedings paper
Title:	Learning a Neural 3D Texture Space from 2D Exemplars
Event:	2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1109/CVPR42600.2020.00838
Publisher version:	http://dx.doi.org/10.1109/CVPR42600.2020.00838
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Three-dimensional displays, Two dimensional displays, Stochastic processes, Interpolation, Decoding, Graphics, Training
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI:	https://discovery.ucl.ac.uk/id/eprint/10117241