IEEE Comput Graph Appl. 2022 Jul-Aug;42(4):72-79. doi: 10.1109/MCG.2021.3115181. Epub 2022 Jul 15.
This article presents a data-driven approach for beautifying freehand sketches. Our key premise is that the artist-drawn vector can be used to sketch visually appealing shapes, such as local shapes with a clean appearance and better global visual properties (e.g., symmetry). However, these merits may not apply to all object categories. In this article, we use a neural network to represent local and global merits across different object categories to design our beautification method. First, we match sample points between input sketches and the collected vector shapes using the extracted feature representations. Then, we design an optimization problem to ensure resemblance between the deformed sketch and vector shape in the representation space while preserving the semantic meaning and style of the original sketch. Finally, we demonstrate our method on sketches across different shape categories.
本文提出了一种基于数据的手绘草图美化方法。我们的主要前提是艺术家绘制的向量可以用于绘制具有吸引力的形状，例如外观整洁的局部形状和更好的全局视觉属性（例如对称）。然而，这些优点可能不适用于所有对象类别。在本文中，我们使用神经网络来表示不同对象类别的局部和全局优点，以设计我们的美化方法。首先，我们使用提取的特征表示在输入草图和收集的向量形状之间匹配样本点。然后，我们设计了一个优化问题，以确保在表示空间中变形草图和向量形状之间的相似性，同时保留原始草图的语义和风格。最后，我们在不同形状类别的草图上展示了我们的方法。
