Learning to Cartoonize Using White-Box Cartoon Representations
Learning to Cartoonize Using White-Box Cartoon Representations
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DOI：
10.1109/cvpr42600.2020.00811
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Xinrui Wang-;Jinze Yu
中科院分区：
文献类型：
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作者：
Xinrui Wang-;Jinze Yu
提出了一种图像卡通化的方法。通过观察卡通绘画行为和咨询艺术家，我们提出了从图像中分别识别三种白盒表示：包含卡通图像光滑表面的表面表示，涉及赛璐珞风格工作流程中稀疏色块和扁平化全局内容的结构表示，以及反映卡通图像高频纹理，轮廓和细节的纹理表示。生成对抗网络（GAN）框架用于学习提取的表示和卡通化图像。我们的方法的学习目标分别基于每个提取的表示，使我们的框架可控和可调。这使我们的方法能够满足艺术家在不同风格和不同用例中的需求。定性比较和定量分析，以及用户研究，已经进行了验证这种方法的有效性，我们的方法优于以前的方法在所有的比较。最后，消融研究表明，在我们的框架中的每个组件的影响。
This paper presents an approach for image cartoonization. By observing the cartoon painting behavior and consulting artists, we propose to separately identify three white-box representations from images: the surface representation that contains smooth surface of cartoon images, the structure representation that refers to the sparse color-blocks and flatten global content in the celluloid style workflow, and the texture representation that reflects high-frequency texture, contours and details in cartoon images. A Generative Adversarial Network (GAN) framework is used to learn the extracted representations and to cartoonize images. The learning objectives of our method are separately based on each extracted representations, making our framework controllable and adjustable. This enables our approach to meet artists' requirements in different styles and diverse use cases. Qualitative comparisons and quantitative analyses, as well as user studies, have been conducted to validate the effectiveness of this approach, and our method outperforms previous methods in all comparisons. Finally, the ablation study demonstrates the influence of each component in our framework.