Please use this identifier to cite or link to this item:
https://repository.cihe.edu.hk/jspui/handle/cihe/3544
Title: | End-to-end line drawing vectorization | Author(s): | Li, Chengze Liu, Xueting |
Author(s): | Liu, H. Wong, T.-T. |
Issue Date: | 2022 | Publisher: | AAAI Press | Related Publication(s): | Proceedings of the 36th AAAI Conference on Artificial Intelligence | Volume: | 36 | Issue: | 4 | Start page: | 4559 | End page: | 4566 | 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. |
URI: | https://repository.cihe.edu.hk/jspui/handle/cihe/3544 | CIHE Affiliated Publication: | Yes |
Appears in Collections: | CIS Publication |
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