The authors present a framework for interactive design of new image stylisations using a wide range of predefined filter blocks. Both novel and off-the-shelf image filtering and rendering techniques are extended and combined to allow the user to unleash their creativity to intuitively invent, modify, and tune new styles from a given set of filters. In parallel to this manual design, they propose a novel procedural approach that automatically assembles sequences of filters, leading to unique and novel styles. An important aim of the authors’ framework is to allow for interactive exploration and design, as well as to enable videos and camera streams to be stylised on the fly. In order to achieve this real-time performance, they use the Best Linear Adaptive Enhancement (BLADE) framework – an interpretable shallow machine learning method that simulates complex filter blocks in real time. Their representative results include over a dozen styles designed using their interactive tool, a set of styles created procedurally, and new filters trained with their BLADE approach.

中文翻译：

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图像样式化：从预定义到个性化

作者提出了使用广泛的预定义滤镜块进行交互式交互设计新图像样式的框架。新颖和现成的图像过滤和渲染技术都得到扩展和组合，以允许用户释放创造力，以直观地发明，修改和调整给定过滤器集的新样式。与该手动设计同时，他们提出了一种新颖的程序方法，该方法可以自动组装过滤器序列，从而产生独特而新颖的样式。作者框架的一个重要目标是允许进行交互式探索和设计，以及使视频和摄像机流能够即时进行样式化。为了获得这种实时性能，他们使用最佳线性自适应增强（BLADE）框架–一种可解释的浅层机器学习方法，可实时模拟复杂的滤波器块。他们的代表性成果包括使用其交互式工具设计的十几种样式，一组过程式创建的样式以及使用其BLADE方法训练的新过滤器。