End-to-End Line Drawing Vectorization
Keywords:Domain(s) Of Application (APP), Computer Vision (CV), Machine Learning (ML)
AbstractVector 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.
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
AAAI Technical Track on Domain(s) Of Application