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.
Due to the popularity of comics, many related research topics, such as @cite_31 , @cite_25 and @cite_13 have drawn considerable research attention in computer graphics community. Particularly, several techniques have been studied to facilitate layout generation. For example, Arai @cite_35 and Pang @cite_26 studied how to automatically extract each panel from e-comics; and display e-comics on different devices. In order to convert conversational videos to comics, Jing @cite_27 made use of a rule based optimization scheme for layout generation. Cao @cite_17 presented a generative probabilistic framework to arrange input artworks into a manga page, and then used optimization techniques to refine it. Furthermore, Cao @cite_33 took text balloons and picture subjects into consideration for manga layout generation and guided the reader's attention. However, in our poster generation, one has to consider both texts and graphical elements composition within each panel, which has not been discussed previously.
@cite_35 @cite_26 @cite_33 @cite_27 @cite_31 @cite_13 @cite_25 @cite_17
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A method for automatic e-comic scene frame extraction is proposed for displaying large scale of comic scenes onto relatively small size of display screen such as mobile devices. In line with the rapid development of mobile devices, reading comic in small screen mobile devices is also demanded and required. The challenge in providing e-comics for small screen is how to separate comic scene frames and display it in the right order to read. We propose Automatic E-Comic Frame Extraction (ACFE) for extraction of scene frames from a digital comic page automatically. ACFE is based on the blob extraction method using connected component labeling algorithm, together with a filter combination pre-processing and efficient method for detection of line between frames. Experimental results show that 91.483 percent of 634 pages in 5 digital comics are successfully extracted into scene frames by the proposed method. Automatically extracting frames panels from digital comic pages is crucial for techniques that facilitate comic reading on mobile devices with limited display areas. However, automatic panel extraction for manga, i.e., Japanese comics, can be especially challenging, largely because of its complex panel layout design mixed with various visual symbols throughout the page. In this paper, we propose a robust method for automatically extracting panels from digital manga pages. Our method first extracts the panel block by closing open panels and identifying a page background mask. It then performs a recursive binary splitting to partition the panel block into a set of sub-blocks, where an optimal splitting line at each recursive level is determined adaptively. We introduce in this paper a new approach that conveniently converts conversational videos into comics with manga-style layout. With our approach, the manga-style layout of a comic page is achieved in a content-driven manner, and the main components, including panels and word balloons, that constitute a visually pleasing comic page are intelligently organized . Our approach extracts key frames on speakers by using a speaker detection technique such that word balloons can be placed near the corresponding speakers. We qualitatively measure the information contained in a comic page. With the initial layout automatically determined, the final comic page is obtained by maximizing such a measure and optimizing the parameters relating to the optimal display of comics. An efficient Markov chain Monte Carlo sampling algorithm is designed for the optimization. Our user study demonstrates that users much prefer our manga-style comics to purely Western style comics. Extensive experiments and comparisons against previous work also verify the effectiveness of our approach. The existing content aware image retargeting methods are mainly suitable for natural images, and do not perform well on line drawings. Such methods tend to regard homogeneous areas as less important. For line drawings, this is not always true. This research proposes a novel method to present "thumbnails" of episodes of digitized comics, in order to improve the efficiency of comic search. Comic episode thumbnails are generated based on image analysis technologies developed especially for comic images. Namely, the following procedures are developed for our system: automatic comic frame segmentation, text balloon extraction, and a linear regression based model to calculate the importance score of each extracted frame. The system then selects frames from each episode with high importance score, and aligns the selected frames to create the episode thumbnail, which is presented to the system user as a compact preview of the episode. User experiments conducted with actual Japanese comic images prove that the proposed method significantly decreases the time necessary to search for specific episodes from a large scaled comic data collection. 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.