# Curve-based Neural Style Transfer

arXiv: https://arxiv.org/abs/2401.08579 · HTML: https://mlsanity.com/paper/2401.08579/

## 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.

## Builds on (most cited first, 6 of 6)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [Learning Deep Sketch Abstraction](https://mlsanity.com/paper/1804.04804/) (2018)
- [Deep Vectorization of Technical Drawings](https://mlsanity.com/paper/2003.05471/) (2020)
- [Automating Style Analysis and Visualization With Explainable AI -- Case Studies on Brand Recognition](https://mlsanity.com/paper/2306.03021/) (2023)

## 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.