论文标题
指导用户在哪里提供颜色提示，以通过无监督区域优先级为有效的交互式草图着色
Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization
论文作者
论文摘要
现有的深层交互式着色模型集中在利用各种类型的交互作用的方法上，例如点色彩提示，涂鸦或自然语言文本，作为反映用户在运行时意图的方法。但是，另一种积极地告知用户最有效区域的方法为素描图像着色的提示提供了详尽的探索。本文提出了一个新颖的模型引导深度交互式着色框架，该框架通过在着色模型中优先考虑区域来减少所需的用户交互。我们的方法（称为GuidingPainter）优先考虑这些模型最需要颜色提示的区域，而不仅仅是依靠用户在何处提供颜色提示的手动决定。在我们的广泛实验中，我们表明我们的方法在常规指标（例如PSNR和FID）方面优于现有的交互式着色方法，并且减少了需要的相互作用量。
Existing deep interactive colorization models have focused on ways to utilize various types of interactions, such as point-wise color hints, scribbles, or natural-language texts, as methods to reflect a user's intent at runtime. However, another approach, which actively informs the user of the most effective regions to give hints for sketch image colorization, has been under-explored. This paper proposes a novel model-guided deep interactive colorization framework that reduces the required amount of user interactions, by prioritizing the regions in a colorization model. Our method, called GuidingPainter, prioritizes these regions where the model most needs a color hint, rather than just relying on the user's manual decision on where to give a color hint. In our extensive experiments, we show that our approach outperforms existing interactive colorization methods in terms of the conventional metrics, such as PSNR and FID, and reduces required amount of interactions.
