Data-Driven Sketch Beautification With Neural Feature Representation
Data-Driven Sketch Beautification With Neural Feature Representation
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DOI：
10.1109/mcg.2021.3115181
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发表时间：
2021-09
影响因子：
1.8
通讯作者：
I-Chao Shen
中科院分区：
文献类型：
--
作者：
I-Chao Shen
本文提出了一种数据驱动的手绘素描美化方法。我们的关键前提是，艺术家绘制的矢量可以用于绘制视觉上有吸引力的形状，例如外观干净且具有更好的全局视觉属性(例如，对称性)的局部形状。然而，这些优点可能并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来匹配输入草图和收集的矢量形状之间的样本点。然后，我们设计了一个优化问题，以确保变形后的草图与表示空间中的矢量形状相似，同时保持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演示了我们的方法。
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.