Neural Image Abstraction Using Long Smoothing B-Splines

• URL: http://arxiv.org/abs/2511.05360v1
• Date: Fri, 07 Nov 2025 15:50:48 GMT
• Title: Neural Image Abstraction Using Long Smoothing B-Splines
• Authors: Daniel Berio, Michael Stroh, Sylvain Calinon, Frederic Fol Leymarie, Oliver Deussen, Ariel Shamir,
• Abstract summary: We show how to generate smooth and arbitrarily long paths within image-based deep learning systems.<n>We take advantage of derivative-based smoothing costs for parametric control of fidelity vs. simplicity tradeoffs.
• Score: 33.22485341851476
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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