Berio, D., Stroh, M., Calinon, S., Fol Leymarie, F., Deussen, O. and Shamir, A. (2025)
Neural Image Abstraction Using Long Smoothing B-splines
ACM Transactions on Graphics (ToG).
Abstract
We integrate smoothing B-splines into a standard differentiable vector graphics (DiffVG) pipeline through linear mapping, and show how this can be used to generate smooth and arbitrarily long paths within image-based deep learning systems. We take advantage of derivative-based smoothing costs for parametric control of fidelity vs. simplicity tradeoffs, while also enabling stylization control in geometric and image spaces. The proposed pipeline is compatible with recent vector graphics generation and vectorization methods. We demonstrate the versatility of our approach with four applications aimed at the generation of stylized vector graphics: stylized space-filling path generation, stroke-based image abstraction, closed-area image abstraction, and stylized text generation.
Bibtex reference
@article{Berio25ToG,
author={Berio, D. and Stroh, M. and Calinon, S. and Fol Leymarie, F. and Deussen, O. and Shamir, A.},
title={Neural Image Abstraction Using Long Smoothing {B}-splines},
journal={ACM Transactions on Graphics (ToG)},
year={2025}
}