What characterizes personalities of graphic designs?

TLDR

Graphic designers manipulate visual elements to convey personalities such as cute or mysterious, yet identifying the factors that shape these personalities is difficult because designs result from many decisions on font, color, image, and layout. We aim to answer what characterizes the personality of a graphic design. We propose a deep learning framework that trains a convolutional neural network, the personality scoring network, to estimate personality scores from web data and systematically analyze how factors such as color, font, and layout influence personality at pixel, region, and element levels. The personality scoring network learns a visual representation that predicts design personality and enables practical applications such as element‑level design suggestions and example‑based personality transfer.

Abstract

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.