Neural Strokes: Stylized Line Drawing of 3D Shapes

Difan Liu, Matthew Fisher, Aaron Hertzmann, Evangelos Kalogerakis

ICCV 2021 pp. 14204-14213

doi:10.1109/ICCV48922.2021.01394 /iccv/2021/liu2021iccv-neural/

Abstract

This paper introduces a model for producing stylized line drawings from 3D shapes. The model takes a 3D shape and a viewpoint as input, and outputs a drawing with textured strokes, with variations in stroke thickness, deformation, and color learned from an artist's style. The model is fully differentiable. We train its parameters from a single training drawing of another 3D shape. We show that, in contrast to previous image-based methods, the use of a geometric representation of 3D shape and 2D strokes allows the model to transfer important aspects of shape and texture style while preserving contours. Our method outputs the resulting drawing in a vector representation, enabling richer downstream analysis or editing in interactive applications.

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Cite

Text

Liu et al. "Neural Strokes: Stylized Line Drawing of 3D Shapes." International Conference on Computer Vision, 2021. doi:10.1109/ICCV48922.2021.01394

Markdown

[Liu et al. "Neural Strokes: Stylized Line Drawing of 3D Shapes." International Conference on Computer Vision, 2021.](https://mlanthology.org/iccv/2021/liu2021iccv-neural/) doi:10.1109/ICCV48922.2021.01394

BibTeX

@inproceedings{liu2021iccv-neural,
  title     = {{Neural Strokes: Stylized Line Drawing of 3D Shapes}},
  author    = {Liu, Difan and Fisher, Matthew and Hertzmann, Aaron and Kalogerakis, Evangelos},
  booktitle = {International Conference on Computer Vision},
  year      = {2021},
  pages     = {14204-14213},
  doi       = {10.1109/ICCV48922.2021.01394},
  url       = {https://mlanthology.org/iccv/2021/liu2021iccv-neural/}
}