Neural Style Transfer for Vector Graphics

Ivan Jarsky, Valeria Efimova, Artyom Chebykin, Viacheslav Shalamov, Andrey Filchenkov

2024

Abstract

Neural style transfer draws researchers’ attention, but the interest focuses on bitmap images. Various models have been developed for bitmap image generation both online and offline with arbitrary and pre-trained styles. However, the style transfer between vector images has not almost been considered. Our research shows that applying standard content and style losses insignificantly changes the vector image drawing style because the structure of vector primitives differs a lot from pixels. To handle this problem, we introduce new loss functions. We also develop a new method based on differentiable rasterization that uses these loss functions and can change the color and shape parameters of the content image corresponding to the drawing of the style image. Qualitative experiments demonstrate the effectiveness of the proposed VectorNST method compared with the state-of-the-art neural style transfer approaches for bitmap images and the only existing approach for stylizing vector images, DiffVG. Although the proposed model does not achieve the quality and smoothness of style transfer between bitmap images, we consider our work an important early step in this area. VectorNST code and demo service are available at https://github.com/IzhanVarsky/VectorNST.

Paper Citation

in Harvard Style

Jarsky I., Efimova V., Chebykin A., Shalamov V. and Filchenkov A. (2024). Neural Style Transfer for Vector Graphics. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 686-693. DOI: 10.5220/0012438200003660

in Bibtex Style

@conference{visapp24,
author={Ivan Jarsky and Valeria Efimova and Artyom Chebykin and Viacheslav Shalamov and Andrey Filchenkov},
title={Neural Style Transfer for Vector Graphics},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={686-693},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012438200003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Neural Style Transfer for Vector Graphics
SN - 978-989-758-679-8
AU - Jarsky I.
AU - Efimova V.
AU - Chebykin A.
AU - Shalamov V.
AU - Filchenkov A.
PY - 2024
SP - 686
EP - 693
DO - 10.5220/0012438200003660
PB - SciTePress