Summary of Curve-based Neural Style Transfer, by Yu-hsuan Chen et al.
Curve-based Neural Style Transfer
by Yu-hsuan Chen, Levent Burak Kara, Jonathan Cagan
First submitted to arxiv on: 3 Oct 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed parametric style transfer framework is designed specifically for curve-based design sketches, addressing traditional challenges faced by neural style transfer methods. The approach utilizes shape-editing rules, efficient conversion techniques, and fine-tuned VGG19 on ImageNet-Sketch to enhance its role as a feature pyramid network. This harmonizes intuitive curve-based imagery with rule-based editing, potentially elevating the practice of style transfer in product design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes it easier to combine different styles in design sketches. It uses special rules and techniques to edit curves and convert them into pixels, making it better at extracting styles from images. This could make designing products more efficient and creative. |
Keywords
» Artificial intelligence » Feature pyramid » Style transfer
