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.
Graphical design has been studied extensively in computer graphics community. This involves several related, yet different topics. Geigel @cite_28 made use of genetic algorithm @cite_2 @cite_18 for , which addresses the placement of each photo in an album. Yu @cite_10 automatically synthesized furniture objects arrangements using simulated annealing algorithm. In contrast, Merrell @cite_5 applied some simple design guidelines to solve a similar problem. Other graphical design problems such as @cite_12 , @cite_15 , and @cite_24 have also been studied. These works often present an optimization framework along with some design guidelines to synthesize and evaluate plausible layouts.
@cite_18 @cite_28 @cite_24 @cite_2 @cite_5 @cite_15 @cite_10 @cite_12
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We present a system that automatically synthesizes indoor scenes realistically populated by a variety of furniture objects. Given examples of sensibly furnished indoor scenes, our system extracts, in advance, hierarchical and spatial relationships for various furniture objects, encoding them into priors associated with ergonomic factors, such as visibility and accessibility, which are assembled into a cost function whose optimization yields realistic furniture arrangements. To deal with the prohibitively large search space, the cost function is optimized by simulated annealing using a Metropolis-Hastings state search step. We demonstrate that our system can synthesize multiple realistic furniture arrangements and, through a perceptual study, investigate whether there is a significant difference in the perceived functionality of the automatically synthesized results relative to furniture arrangements produced by human designers. This paper presents an approach for automatically creating graphic design layouts using a new energy-based model derived from design principles. The model includes several new algorithms for analyzing graphic designs, including the prediction of perceived importance, alignment detection, and hierarchical segmentation. Given the model, we use optimization to synthesize new layouts for a variety of single-page graphic designs. Model parameters are learned with Nonlinear Inverse Optimization (NIO) from a small number of example layouts. To demonstrate our approach, we show results for applications including generating design layouts in various styles, retargeting designs to new sizes, and improving existing designs. We also compare our automatic results with designs created using crowdsourcing and show that our approach performs slightly better than novice designers. Although existing GUIs have a sense of space, they provide no sense of place. Numerous studies report that users misplace files and have trouble wayfinding in virtual worlds despite the fact that people have remarkable visual and spatial abilities. This issue is considered in the human-computer interface field and has been addressed with alternate display navigation schemes. Our paper presents a fundamentally graphics based approach to this 'lost in hyperspace' problem. Specifically, we propose that spatial display of files is not sufficient to engage our visual skills; scenery (distinctive visual appearance) is needed as well. While scenery (in the form of custom icon assignments) is already possible in current operating systems, few if any users take the time to manually assign icons to all their files. As such, our proposal is to generate visually distinctive icons ("VisualIDs") automatically, while allowing the user to replace the icon if desired. The paper discusses psychological and conceptual issues relating to icons, visual memory, and the necessary relation of scenery to data. A particular icon generation algorithm is described; subjects using these icons in simulated file search and recall tasks show significantly improved performance with little effort. Although the incorporation of scenery in a graphical user interface will introduce many new (and interesting) design problems that cannot be addressed in this paper, we show that automatically created scenery is both beneficial and feasible. We propose a novel hierarchical approach for text-to-image synthesis by inferring semantic layout. Instead of learning a direct mapping from text to image, our algorithm decomposes the generation process into multiple steps, in which it first constructs a semantic layout from the text by the layout generator and converts the layout to an image by the image generator. The proposed layout generator progressively constructs a semantic layout in a coarse-to-fine manner by generating object bounding boxes and refining each box by estimating object shapes inside the box. The image generator synthesizes an image conditioned on the inferred semantic layout, which provides a useful semantic structure of an image matching with the text description. Our model not only generates semantically more meaningful images, but also allows automatic annotation of generated images and user-controlled generation process by modifying the generated scene layout. We demonstrate the capability of the proposed model on challenging MS-COCO dataset and show that the model can substantially improve the image quality, interpretability of output and semantic alignment to input text over existing approaches.