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.
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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 |
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