Journal ArticleDOI
Automatic Generation of Visual-Textual Presentation Layout
TLDR
This article presents a system to automatically generate visual-textual presentation layouts by investigating a set of aesthetic design principles, through which an average user can easily create visually appealing layouts, and demonstrates that the designs achieve the best reading experience compared with the reimplementation of parts of existing state-of-the-art designs.Abstract:
Visual-textual presentation layout (e.g., digital magazine cover, poster, Power Point slides, and any other rich media), which combines beautiful image and overlaid readable texts, can result in an eye candy touch to attract users’ attention. The designing of visual-textual presentation layout is therefore becoming ubiquitous in both commercially printed publications and online digital magazines. However, handcrafting aesthetically compelling layouts still remains challenging for many small businesses and amateur users. This article presents a system to automatically generate visual-textual presentation layouts by investigating a set of aesthetic design principles, through which an average user can easily create visually appealing layouts. The system is attributed with a set of topic-dependent layout templates and a computational framework integrating high-level aesthetic principles (in a top-down manner) and low-level image features (in a bottom-up manner). The layout templates, designed with prior knowledge from domain experts, define spatial layouts, semantic colors, harmonic color models, and font emotion and size constraints. We formulate the typography as an energy optimization problem by minimizing the cost of text intrusion, the utility of visual space, and the mismatch of information importance in perception and semantics, constrained by the automatically selected template and further preserving color harmonization. We demonstrate that our designs achieve the best reading experience compared with the reimplementation of parts of existing state-of-the-art designs through a series of user studies.read more
Citations
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Journal ArticleDOI
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
TL;DR: It is demonstrated that D3Net can be used to efficiently extract salient object masks from real scenes, enabling effective background-changing application with a speed of 65 frames/s on a single GPU.
Journal ArticleDOI
Image Captioning with Deep Bidirectional LSTMs and Multi-Task Learning
TL;DR: It is demonstrated that Bi-LSTM models achieve highly competitive performance on both caption generation and image-sentence retrieval even without integrating an additional mechanism (e.g., object detection, attention model), and it is proved that multi-task learning is beneficial to increase model generality and gain performance.
Journal ArticleDOI
Content-aware generative modeling of graphic design layouts
TL;DR: This paper proposes a deep generative model for graphic design layouts that is able to synthesize layout designs based on the visual and textual semantics of user inputs, and internally learns powerful features that capture the subtle interaction between contents and layouts, which are useful for layout-aware design retrieval.
Proceedings ArticleDOI
guigan: Learning to Generate GUI Designs Using Generative Adversarial Networks
TL;DR: Gui et al. as discussed by the authors developed a model GUIGAN to automatically generate GUI designs based on SeqGAN by modeling the GUI component style compatibility and GUI structure, which is similar to natural language generation.
Proceedings ArticleDOI
Vinci: An Intelligent Graphic Design System for Generating Advertising Posters
TL;DR: In this article, a deep generative model is used to match the product image with a set of design elements and layouts for generating an aesthetic poster, and the system also integrates online editing-feedback that supports users in editing the posters and updating the generated results with their design preference.
References
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Proceedings ArticleDOI
Seam carving for content-aware image resizing
Shai Avidan,Ariel Shamir +1 more
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Proceedings ArticleDOI
Global contrast based salient region detection
Journal ArticleDOI
Seam carving for content-aware image resizing
AvidanShai,ShamirAriel +1 more
TL;DR: In this article, a simple image operator called seam carving is presented that supports content-aware resizing of images by considering the image content as well as the geometric constraints of the image.