End-to-End Line Drawing Vectorization
Abstract
Vector graphics is broadly used in a variety of forms, such as illustrations, logos, posters, billboards, and printed ads. Despite its broad use, many artists still prefer to draw with pen and paper, which leads to a high demand of converting raster designs into the vector form. In particular, line drawing is a primary art and attracts many research efforts in automatically converting raster line drawings to vector form. However, the existing methods generally adopt a two-step approach, stroke segmentation and vectorization. Without vector guidance, the raster-based stroke segmentation frequently obtains unsatisfying segmentation results, such as over-grouped strokes and broken strokes. In this paper, we make an attempt in proposing an end-to-end vectorization method which directly generates vectorized stroke primitives from raster line drawing in one step. We propose a Transformer-based framework to perform stroke tracing like human does in an automatic stroke-by-stroke way with a novel stroke feature representation and multi-modal supervision to achieve vectorization with high quality and fidelity. Qualitative and quantitative evaluations show that our method achieves state of the art performance.
Cite
Text
Liu et al. "End-to-End Line Drawing Vectorization." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I4.20379Markdown
[Liu et al. "End-to-End Line Drawing Vectorization." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/liu2022aaai-end/) doi:10.1609/AAAI.V36I4.20379BibTeX
@inproceedings{liu2022aaai-end,
title = {{End-to-End Line Drawing Vectorization}},
author = {Liu, Hanyuan and Li, Chengze and Liu, Xueting and Wong, Tien-Tsin},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {4559-4566},
doi = {10.1609/AAAI.V36I4.20379},
url = {https://mlanthology.org/aaai/2022/liu2022aaai-end/}
}