End-to-End Line Drawing Vectorization
DOI:
https://doi.org/10.1609/aaai.v36i4.20379Keywords:
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.Downloads
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