ML Anthology

Fast Texture Synthesis via Pseudo Optimizer

Wu Shi, Yu Qiao
CVPR 2020
doi:10.1109/CVPR42600.2020.00554 /cvpr/2020/shi2020cvpr-fast/

Abstract

Texture synthesis using deep neural networks can generate high quality and diversified textures. However, it usually requires a heavy optimization process. The following works accelerate the process by using feed-forward networks, but at the cost of scalability. diversity or quality. We propose a new efficient method that aims to simulate the optimization process while retains most of the properties. Our method takes a noise image and the gradients from a descriptor network as inputs, and synthesize a refined image with respect to the target image. The proposed method can synthesize images with better quality and diversity than the other fast synthesis methods do. Moreover, our method trained on a large scale dataset can generalize to synthesize unseen textures.

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Cite

Text

Shi and Qiao. "Fast Texture Synthesis via Pseudo Optimizer." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. doi:10.1109/CVPR42600.2020.00554

Markdown

[Shi and Qiao. "Fast Texture Synthesis via Pseudo Optimizer." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.](https://mlanthology.org/cvpr/2020/shi2020cvpr-fast/) doi:10.1109/CVPR42600.2020.00554

BibTeX

@inproceedings{shi2020cvpr-fast,
  title     = {{Fast Texture Synthesis via Pseudo Optimizer}},
  author    = {Shi, Wu and Qiao, Yu},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2020},
  doi       = {10.1109/CVPR42600.2020.00554},
  url       = {https://mlanthology.org/cvpr/2020/shi2020cvpr-fast/}
}