Texture plays an important role in cartoon illustrations to display object materials and enrich visual experiences. Unfortunately, manually designing and drawing an appropriate texture is not easy even for proficient artists, let alone novice or amateur people. While there exist tons of textures on the Internet, it is not easy to pick an appropriate one using traditional text-based search engines. Though several texture pickers have been proposed, they still require the users to browse the textures by themselves, which is still labor-intensive and time-consuming. In this paper, an automatic texture recommendation system is proposed for recommending multiple textures to replace a set of user-specified regions in a cartoon illustration with visually pleasant look. Two measurements, the suitability measurement and the style-consistency measurement, are proposed to make sure that the recommended textures are suitable for cartoon illustration and at the same time mutually consistent in style. The suitability is measured based on the synthesizability, cartoonity, and region fitness of textures. The style-consistency is predicted using a learning-based solution since it is subjective to judge whether two textures are consistent in style. An optimization problem is formulated and solved via the genetic algorithm. Our method is validated on various cartoon illustrations, and convincing results are obtained.

纹理在卡通插画中发挥着重要作用，可以展示物体材质，丰富视觉体验。不幸的是，即使对于熟练的艺术家来说，手动设计和绘制合适的纹理也不容易，更不用说新手或业余爱好者了。虽然互联网上存在大量纹理，但使用传统的基于文本的搜索引擎选择合适的纹理并不容易。尽管已经提出了几种纹理选择器，但它们仍然需要用户自己浏览纹理，这仍然是劳动密集型和耗时的。在本文中，提出了一种自动纹理推荐系统，用于推荐多个纹理来替换具有视觉愉悦外观的卡通插图中的一组用户指定区域。提出了两种测量方法，即适合性测量和风格一致性测量，以确保推荐的纹理适合卡通插画，同时风格相互一致。适合性是根据纹理的可合成性、卡通性和区域适合度来衡量的。使用基于学习的解决方案来预测风格一致性，因为判断两个纹理的风格是否一致是主观的。通过遗传算法制定和解决优化问题。我们的方法在各种卡通插图上进行了验证，并获得了令人信服的结果。