1University College London 2 Adobe
CVPR 2020
We propose 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. The key idea is a hard-coded, tunable and differentiable step that feeds multiple transformed random 2D or 3D fields into an MLP that can be sampled over infinite domains. 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
| Wood | Grass | Marble | Rust |
|---|
@inproceedings{henzler2020neuraltexture,
title={Learning a Neural 3D Texture Space from 2D Exemplars},
author={Henzler, Philipp and Mitra, Niloy J and and Ritschel, Tobias},
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}
month={June},
year={2019}
}
