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首页> 外文期刊>ACM Transactions on Graphics >Real-Time Data-Driven Interactive Rough Sketch Inking
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Real-Time Data-Driven Interactive Rough Sketch Inking

机译：实时数据驱动的交互式粗略草图绘制

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摘要

We present an interactive approach for inking, which is the process of turning a pencil rough sketch into a clean line drawing. The approach, which we call the Smart Inker, consists of several "smart" tools that intuitively react to user input, while guided by the input rough sketch, to efficiently and naturally connect lines, erase shading, and fine-tune the line drawing output. Our approach is data-driven: the tools are based on fully convolutional networks, which we train to exploit both the user edits and inaccurate rough sketch to produce accurate line drawings, allowing high-performance interactive editing in real-time on a variety of challenging rough sketch images. For the training of the tools, we developed two key techniques: one is the creation of training data by simulation of vague and quick user edits; the other is a line normalization based on learning from vector data. These techniques, in combination with our sketch-specific data augmentation, allow us to train the tools on heterogeneous data without actual user interaction. We validate our approach with an in-depth user study, comparing it with professional illustration software, and show that our approach is able to reduce inking time by a factor of 1.8x, while improving the results of amateur users.
机译：我们提出了一种交互式的上墨方法，即将铅笔粗略的草图变成干净的线条图的过程。这种方法，我们称为Smart Inker，由多个“智能”工具组成，它们可以在输入粗略草图的引导下直观地响应用户输入，以有效自然地连接线，消除阴影并微调线绘图输出。我们的方法是数据驱动的：这些工具基于完全卷积的网络，我们对其进行了训练，以利用用户编辑和不准确的粗略草图来生成准确的线图，从而可以在各种挑战下实时进行高性能的交互式编辑草图图像。对于工具的培训，我们开发了两种关键技术：一种是通过模拟模糊和快速的用户编辑来创建培训数据。另一个是基于从向量数据中学习的线归一化。这些技术与我们特定于草图的数据扩充相结合，使我们能够在无需实际用户交互的情况下针对异构数据训练工具。我们通过深入的用户研究验证了我们的方法，并将其与专业的插图软件进行了比较，并表明我们的方法能够将上墨时间减少1.8倍，同时改善了业余用户的效果。

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