IEEE Trans Vis Comput Graph. 2023 Jun;29(6):3093-3104. doi: 10.1109/TVCG.2022.3151617. Epub 2023 May 3.
During the creation of graphic designs, individuals inevitably spend a lot of time and effort on adjusting visual attributes (e.g., positions, colors, and fonts) of elements to make them more aesthetically pleasing. It is a trial-and-error process, requires repetitive edits, and relies on good design knowledge. In this work, we seek to alleviate such difficulty by automatically suggesting aesthetic improvements, i.e., taking an existing design as the input and generating a refined version with improved aesthetic quality as the output. This goal presents two challenges: proposing a refined design based on the user-given one, and assessing whether the new design is better aesthetically. To cope with these challenges, we propose a design principle-guided candidate generation stage and a data-driven candidate evaluation stage. In the candidate generation stage, we generate candidate designs by leveraging design principles as the guidance to make changes around the existing design. In the candidate evaluation stage, we learn a ranking model upon a dataset that can reflect humans' aesthetic preference, and use it to choose the most aesthetically pleasing one from the generated candidates. We implement a prototype system on presentation slides and demonstrate the effectiveness of our approach through quantitative analysis, sample results, and user studies.
在图形设计创作过程中，个体不可避免地要花费大量时间和精力来调整元素的视觉属性（例如位置、颜色和字体），使其更具美感。这是一个反复试验的过程，需要重复编辑，并依赖于良好的设计知识。在这项工作中，我们试图通过自动建议美学改进来缓解这种困难，即采用现有的设计作为输入，并生成具有改进的美学质量的精制版本作为输出。这一目标提出了两个挑战：基于用户给定的设计提出改进方案，以及评估新设计在美学上是否更好。为了应对这些挑战，我们提出了一个设计原则指导的候选生成阶段和一个数据驱动的候选评估阶段。在候选生成阶段，我们利用设计原则作为指导，在现有设计的基础上进行修改，生成候选设计。在候选评估阶段，我们在一个可以反映人类审美偏好的数据集上学习一个排序模型，并使用它从生成的候选者中选择最具美感的一个。我们在演示幻灯片上实现了一个原型系统，并通过定量分析、样本结果和用户研究证明了我们方法的有效性。
