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.
@article{arxiv.2401.08579,
title = {Curve-based Neural Style Transfer},
author = {Yu-hsuan Chen and Levent Burak Kara and Jonathan Cagan},
journal= {arXiv preprint arXiv:2401.08579},
year = {2024}
}