Abstract
We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style. This is a challenging task due to multiple stroke types (e.g., outline and shading), structural complexity of pencil shading (e.g., hatching), and the lack of aligned training data pairs. To address these challenges, we develop a two-branch model that learns separate filters for generating sketchy outlines and tonal shading from a collection of pencil drawings. We create training data pairs by extracting clean outlines and tonal illustrations from original pencil drawings using image filtering techniques, and we manually label the drawing styles. In addition, our model creates different pencil styles (e.g., line sketchiness and shading style) in a user-controllable manner. Experimental results on different types of pencil drawings show that the proposed algorithm performs favorably against existing methods in terms of quality, diversity and user evaluations.
Abstract (translated)
我们提出了一种高质量的照片到铅笔的翻译方法，对绘图风格进行了精细的控制。由于多种笔画类型（如轮廓和底纹）、铅笔底纹的结构复杂性（如阴影）以及缺乏对齐的训练数据对，这是一项具有挑战性的任务。为了解决这些挑战，我们开发了一个双分支模型，该模型学习从铅笔图集合生成草图轮廓和色调着色的单独过滤器。我们通过使用图像过滤技术从原始铅笔图中提取干净的轮廓和色调插图来创建训练数据对，并手动标记绘图样式。此外，我们的模型以用户可控制的方式创建不同的铅笔样式（例如线条草图和阴影样式）。对不同类型铅笔图的实验结果表明，该算法在质量、多样性和用户评价等方面均优于现有方法。
URL
https://arxiv.org/abs/1903.08682
