Curve-based Neural Style Transfer
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Abstract (arXiv)
This research presents a new parametric style transfer framework specifically designed for curve-based design sketches. In this research, traditional challenges faced by neural style transfer methods in handling binary sketch transformations are effectively addressed through the utilization of parametric shape-editing rules, efficient curve-to-pixel conversion techniques, and the fine-tuning of VGG19 on ImageNet-Sketch, enhancing its role as a feature pyramid network for precise style extraction. By harmonizing intuitive curve-based imagery with rule-based editing, this study holds the potential to significantly enhance design articulation and elevate the practice of style transfer within the realm of product design.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 3 Oct 2023.
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6 of 17 references.
References · 6
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2019Learning Robust Global Representations by Penalizing Local Predictive Powercited by 1,013
- 2019LayoutGAN: Generating Graphic Layouts with Wireframe Discriminatorscited by 131
- 2018Learning Deep Sketch Abstractioncited by 36
- 2020Deep Vectorization of Technical Drawingscited by 23
- 2023Automating Style Analysis and Visualization With Explainable AI -- Case Studies on Brand Recognitioncited by 3
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