ML Anthology

Learning a Neural 3D Texture Space from 2D Exemplars

Philipp Henzler, Niloy J. Mitra, Tobias Ritschel
CVPR 2020
doi:10.1109/CVPR42600.2020.00838 /cvpr/2020/henzler2020cvpr-learning/

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.

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Cite

Text

Henzler et al. "Learning a Neural 3D Texture Space from 2D Exemplars." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00838

Markdown

[Henzler et al. "Learning a Neural 3D Texture Space from 2D Exemplars." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/henzler2020cvpr-learning/) doi:10.1109/CVPR42600.2020.00838

BibTeX

@inproceedings{henzler2020cvpr-learning,
  title     = {{Learning a Neural 3D Texture Space from 2D Exemplars}},
  author    = {Henzler, Philipp and Mitra, Niloy J. and Ritschel, Tobias},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.00838},
  url       = {https://mlanthology.org/cvpr/2020/henzler2020cvpr-learning/}
}