  We explore computational approaches for visual guidance to aid in creating
aesthetically pleasing art and graphic design. Our work complements and builds
on previous work that developed models for how humans look at images. Our
approach comprises three steps. First, we collected a dataset of art
masterpieces and labeled the visual fixations with state-of-art vision models.
Second, we clustered the visual guidance templates of the art masterpieces with
unsupervised learning. Third, we developed a pipeline using generative
adversarial networks to learn the principles of visual guidance and that can
produce aesthetically pleasing layouts. We show that the aesthetic visual
guidance principles can be learned and integrated into a high-dimensional model
and can be queried by the features of graphic elements. We evaluate our
approach by generating layouts on various drawings and graphic designs.
Moreover, our model considers the color and structure of graphic elements when
generating layouts. Consequently, we believe our tool, which generates multiple
aesthetic layout options in seconds, can help artists create beautiful art and
graphic designs.
