Year: 2023
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Institution: Carnegie Mellon University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4391012510