End-to-End Line Drawing Vectorization

Authors

  • Hanyuan Liu The Chinese University of Hong Kong
  • Chengze Li Caritas Institute of Higher Education
  • Xueting Liu Caritas Institute of Higher Education
  • Tien-Tsin Wong The Chinese University of Hong Kong

DOI:

https://doi.org/10.1609/aaai.v36i4.20379

Keywords:

Domain(s) Of Application (APP), Computer Vision (CV), Machine Learning (ML)

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.

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Published

2022-06-28

How to Cite

Liu, H., Li, C., Liu, X., & Wong, T.-T. (2022). End-to-End Line Drawing Vectorization. Proceedings of the AAAI Conference on Artificial Intelligence, 36(4), 4559-4566. https://doi.org/10.1609/aaai.v36i4.20379

Issue

Section

AAAI Technical Track on Domain(s) Of Application