Vector Gradient Stroke Stylized Neural Network Painting
Johnny Lin, Tung-Ju Hsieh · 2023
This study focuses on the oil painting brush style transfer in deep convolutional network-based style painting models. We proposes an SVG gradient vectorization process to preserve brush stroke structures while avoiding the generation of a large number of paths. Most of the images in non-photorealistic rendering painting are raster images, suffering from blurriness and quality degradation when zoomed in, whereas vector graphics offer advantages such as scalability and detail preservation. However, existing SVG vectorization methods struggle with images containing gradient colors. The proposed method involves vectorizing each brush, analyzing the positions of main color tones, and incorporating gradient color control points. Finally, the vectorized brush results are stacked and merged. Experimental results demonstrate that this process can preserve the brush stroke structure and present gradient color effects in non-photorealistic rendering style transfer, enhancing editing flexibility and printing quality for brush style transfer.