Single-Line Drawing Generation via Semantics-Driven Optimization
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913178267418624 |
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| author | Magne, Tanguy Binninger, Alexandre Wiersma, Ruben Sorkine-Hornung, Olga |
| author_facet | Magne, Tanguy Binninger, Alexandre Wiersma, Ruben Sorkine-Hornung, Olga |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01910 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Single-Line Drawing Generation via Semantics-Driven Optimization Magne, Tanguy Binninger, Alexandre Wiersma, Ruben Sorkine-Hornung, Olga Graphics Computer Vision and Pattern Recognition 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. |
| title | Single-Line Drawing Generation via Semantics-Driven Optimization |
| topic | Graphics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2606.01910 |