Learning a Neural 3D Texture Space From 2D Exemplars

Philipp Henzler, Niloy J. Mitra, Tobias Ritschel; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 8356-8364

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.

Related Material


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[bibtex]
@InProceedings{Henzler_2020_CVPR,
author = {Henzler, Philipp and Mitra, Niloy J. and Ritschel, Tobias},
title = {Learning a Neural 3D Texture Space From 2D Exemplars},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}