Curve-based Neural Style Transfer
Yu-hsuan Chen Levent Burak Kara Jonathan Cagan
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.