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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 4: AAAI-22 Technical Tracks 4

End-to-End Line Drawing Vectorization

February 1, 2023

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


Proceedings:

No. 4: AAAI-22 Technical Tracks 4

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Domain(s) Of Application

Downloads:

Download PDF

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.

DOI:

10.1609/aaai.v36i4.20379


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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