用平滑B样条实现图像抽象，生成任意长的流畅矢量路径。
Neural Image Abstraction Using Long Smoothing B-Splines
- 通过线性映射将B样条融入可微矢量图形管道
- 支持风格化控制与保真度-简洁度权衡
- 适用于图像抽象、文字生成等多类矢量图应用
本文通过线性映射将平滑B样条集成到标准可微矢量图形（DiffVG）流程中，展示如何在基于图像的深度学习系统中生成平滑且任意长的路径。利用基于导数的平滑代价函数，实现对保真度与简洁度之间权衡的参数化控制，同时支持几何空间和图像空间中的风格化调控。所提出的流程兼容近期的矢量图形生成与矢量化方法。我们通过四项应用验证其通用性：风格化填满空间的路径生成、基于笔画的图像抽象、闭合区域的图像抽象以及风格化文本生成。
原文摘要 · Abstract (English)
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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