论文标题
具有可控感知因素的互动漫画化
Interactive Cartoonization with Controllable Perceptual Factors
论文作者
论文摘要
卡通化是将自然照片变成卡通风格的任务。以前的深度卡通化方法仅关注端到端翻译，这可能会阻碍编辑性。取而代之的是，我们根据卡通创作过程提出了一种具有纹理和颜色的编辑特征的新颖解决方案。为此，我们设计了一个模型体系结构，以具有单独的解码器，纹理和颜色，以使这些属性解除。在纹理解码器中，我们提出了一个纹理控制器，该控制器使用户能够控制笔划风格和抽象来生成多样化的卡通纹理。我们还引入了HSV颜色增强功能，以诱导网络产生多种可控的颜色翻译。据我们所知，我们的工作是控制推理时的漫画化的第一种深入方法，同时显示出对基准的深刻质量改进。
Cartoonization is a task that renders natural photos into cartoon styles. Previous deep cartoonization methods only have focused on end-to-end translation, which may hinder editability. Instead, we propose a novel solution with editing features of texture and color based on the cartoon creation process. To do that, we design a model architecture to have separate decoders, texture and color, to decouple these attributes. In the texture decoder, we propose a texture controller, which enables a user to control stroke style and abstraction to generate diverse cartoon textures. We also introduce an HSV color augmentation to induce the networks to generate diverse and controllable color translation. To the best of our knowledge, our work is the first deep approach to control the cartoonization at inference while showing profound quality improvement over to baselines.
