Decision of Line Structure beyond Junctions Using U-Net-Based CNN for Line Drawing Rendering

Ryogo Ito, Mitsuhiro Uchida, S. Saito
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Abstract

This paper introduces a U-Net-based neural network to determine line structure beyond junctions more accurately than the previous work [Guo et al. 2019]. In addition to the input of the previous work, we input 3D information to our neural network. We also propose a method to generate the training dataset automatically. The rendering results by the stylized line rendering [Uchida and Saito 2020] show that our neural network improves the streams of strokes.
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基于u - net的CNN线绘制中结点外线结构的确定
本文引入了一种基于u - net的神经网络,以比以前的工作更准确地确定连接点以外的线结构[Guo et al. 2019]。除了前面工作的输入外,我们还将3D信息输入到神经网络中。我们还提出了一种自动生成训练数据集的方法。程式化线条渲染的渲染结果[Uchida and Saito 2020]表明,我们的神经网络改善了笔画流。
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