Learning a Neural 3D Texture Space From 2D Exemplars

Philipp Henzler; Niloy J. Mitra; Tobias Ritschel

CVPR 2020poster104 citations

Learning a Neural 3D Texture Space From 2D Exemplars

Philipp Henzler, Niloy J. Mitra, Tobias Ritschel

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.

BibTeX

@inproceedings{cvpr2020_learninganeural3,
  title = {Learning a Neural 3D Texture Space From 2D Exemplars},
  author = {Philipp Henzler and Niloy J. Mitra and Tobias Ritschel},
  booktitle = {CVPR 2020},
  year = {2020}
}