What Characterizes Personalities of Graphic Designs?
What Characterizes Personalities of Graphic Designs?
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DOI：
10.1145/3197517.3201355
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发表时间：
2018-08-01
影响因子：
6.2
通讯作者：
Lau, Rynson W. H.
中科院分区：
文献类型：
--
作者：
Zhao, Nanxuan;Cao, Ying;Lau, Rynson W. H.
平面设计师经常操纵其设计的整体外观和感觉来传达某些个性（例如可爱、神秘和浪漫），以打动潜在受众并实现业务目标。然而，了解决定设计个性的因素具有挑战性，因为图形设计通常是对字体、颜色、图像和布局等众多因素进行数千个决策的结果。在本文中，我们旨在回答平面设计的个性特征是什么的问题。为此，我们提出了一个深度学习框架，用于探索各种设计因素对平面设计感知个性的影响。我们的框架学习卷积神经网络（称为个性评分网络），通过对爬行的网络数据进行排名来估计图形设计的个性分数。我们的个性评分网络自动学习视觉表示，捕获预测图形设计个性所需的语义。通过我们的个性评分网络，我们系统地、定量地研究各种设计因素（例如颜色、字体和布局）如何影响不同尺度（从像素、区域到元素）的设计个性。我们还展示了我们网络的许多实际应用场景，包括元素级设计建议和基于示例的个性迁移。
Graphic designers often manipulate the overall look and feel of their designs to convey certain personalities (e.g., cute, mysterious and romantic) to impress potential audiences and achieve business goals. However, understanding the factors that determine the personality of a design is challenging, as a graphic design is often a result of thousands of decisions on numerous factors, such as font, color, image, and layout. In this paper, we aim to answer the question of what characterizes the personality of a graphic design. To this end, we propose a deep learning framework for exploring the effects of various design factors on the perceived personalities of graphic designs. Our framework learns a convolutional neural network (called personality scoring network) to estimate the personality scores of graphic designs by ranking the crawled web data. Our personality scoring network automatically learns a visual representation that captures the semantics necessary to predict graphic design personality. With our personality scoring network, we systematically and quantitatively investigate how various design factors (e.g., color, font, and layout) affect design personality across different scales (from pixels, regions to elements). We also demonstrate a number of practical application scenarios of our network, including element-level design suggestion and example-based personality transfer.