Researchers often summarize their work in the form of scientific posters. Posters provide a coherent and efficient way to convey core ideas expressed in scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including attributes of each panel and arrangements of graphical elements are learned and inferred from data. During the inference stage, an MAP inference framework is employed to incorporate some design principles. In order to bridge the gap between panel attributes and the composition within each panel, we also propose a recursive page splitting algorithm to generate the panel layout for a poster. To learn and validate our model, we collect and release a new benchmark dataset, called NJU-Fudan Paper-Poster dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.
Our panel layout generation method is partly inspired by the recent work on manga layout @cite_17 . We use a binary tree to represent the panel layout. By contrast, the manga layout trains a Dirichlet distribution to sample a splitting configuration, and different Dirichlet distribution for each kind of instance has to be trained as a result. Instead, we propose a recursive algorithm to search for the best splitting configuration along a binary tree.
@cite_17
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Manga layout is a core component in manga production, characterized by its unique styles. However, stylistic manga layouts are difficult for novices to produce as it requires hands-on experience and domain knowledge. In this paper, we propose an approach to automatically generate a stylistic manga layout from a set of input artworks with user-specified semantics, thus allowing less-experienced users to create high-quality manga layouts with minimal efforts. We first introduce three parametric style models that encode the unique stylistic aspects of manga layouts, including layout structure, panel importance, and panel shape. Next, we propose a two-stage approach to generate a manga layout: 1) an initial layout is created that best fits the input artworks and layout structure model, according to a generative probabilistic framework; 2) the layout and artwork geometries are jointly refined using an efficient optimization procedure, resulting in a professional-looking manga layout. Through a user study, we demonstrate that our approach enables novice users to easily and quickly produce higher-quality layouts that exhibit realistic manga styles, when compared to a commercially-available manual layout tool.