用语义引导生成单线矢量画，支持文本或图像输入。
Single-Line Drawing Generation via Semantics-Driven Optimization
- 基于评分蒸馏采样优化URBS曲线参数，保证单笔连续
- 生成结果更贴近艺术家风格，优于现有图文模型
- 输出为矢量格式，可直接用于刺绣、激光雕刻等
线条绘画是一种高度表现力的艺术形式，要求艺术家抽象并提炼主题精髓。本文提出首个语义驱动的单线矢量图自动生成方法，可通过文本提示或输入图像进行引导。该方法利用评分蒸馏采样（score distillation sampling）优化统一有理B样条（URBS）曲线参数，确保绘制结果为单一连续笔画。该表示法可精细控制细节程度，额外损失项可调节最终艺术风格。实验表明，该方法在生成效果上优于当前最先进的文本到图像模型及优化流程，产出更美观且更忠实于连续线绘艺术家风格的结果。由于生成的是矢量曲线，可直接应用于刺绣、激光雕刻和金属弯折等下游制造过程。代码与结果见https://github.com/tanguymagne/SLDgen。
原文摘要 · Abstract (English)
Line drawings are a highly expressive art form that requires the artist to abstract and distill the essence of their subject. We present the first semantics-driven method for automatically generating single-line drawings in vector format, guided either by a text prompt describing the concept or an input image depicting it. Our approach leverages score distillation sampling to optimize the parameters of a uniform rational B-spline (URBS) curve, ensuring that the drawing consists of a single continuous stroke by design. This representation provides fine-grained control over the level of detail, while additional loss terms allow us to steer the final artistic style. We demonstrate that our method outperforms state-of-the-art text-to-image models and optimization pipelines for this task, producing results that are both more aesthetically pleasing and more faithful to the style of continuous line drawing artists. Furthermore, because our method generates a vectorized curve, it directly supports downstream fabrication processes such as embroidery, laser engraving and wire bending. Our code and results are available at https://github.com/tanguymagne/SLDgen.
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