Single Line Drawing Generation via Semantics-Driven Optimization

Article
T. Magne, A. Binninger, R. Wiersma, O. Sorkine-Hornung
DOI PDF Code Project

Venue

Computer Graphics Forum

Abstract

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.

Tags

 Generative AI  Vector Graphics  Curves  Geometry  Optimization  Machine Learning

Cite

@article{Magne:SLDgen:2026,
 title = {Single Line Drawing Generation via Semantics-Driven Optimization},
 author = {Magne, Tanguy and Binninger, Alexandre and Wiersma, Ruben and Sorkine-Hornung, Olga},
 journal = {Computer Graphics Forum},
 volume = {44},
 number = {7},
 year = {2026},
 }