Gaudii: An Automated Graphic Design Expert System

Morcilllo, Carlos Gonzalez; Martin, Victor Jose; Fernández, David Vallejo; Sánchez, José Jesús Castro; Albusac, Javier

doi:10.1609/aaai.v24i2.18811

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…Balinsky et al describe measures of alignment in documents [7] as well as an aestheticsdriven layout engine for non-flow documents [8]; however, only a single result is shown, and no evaluation is presented. Gonzalez-Morcillo [9] present a system for creating single-page graphic designs. However, this approach cannot learn different styles and uses a simple layout technique that normally aligns elements to margins.…”

Section: Related Workmentioning

confidence: 99%

Learning Layouts for Single-PageGraphic Designs

O’Donovan

Agarwala

Hertzmann

2014

IEEE Trans. Visual. Comput. Graphics

149

141

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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.

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Section: Related Workmentioning

confidence: 99%

Learning Layouts for Single-PageGraphic Designs

O’Donovan

Agarwala

Hertzmann

2014

IEEE Trans. Visual. Comput. Graphics

149

141

View full text Add to dashboard Cite

show abstract

“…Harrington et al [14] suggest an energy function to measure the aesthetics of a layout. Morcilllo et al [15] create graphic designs by maximizing design quality through cost functions to meet specific design constraints. Similarly, O'Donovan et al [16] propose an energybased model derived from design principles to create layouts, and further develop an interactive tool [17].…”

Section: Related Workmentioning

confidence: 99%

Attribute-Conditioned Layout GAN for Automatic Graphic Design

Yang

Zhang

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Modeling layout is an important first step for graphic design. Recently, methods for generating graphic layouts have progressed, particularly with Generative Adversarial Networks (GANs). However, the problem of specifying the locations and sizes of design elements usually involves constraints with respect to element attributes, such as area, aspect ratio and reading-order. Automating attribute conditional graphic layouts remains a complex and unsolved problem. In this paper, we introduce Attribute-conditioned Layout GAN to incorporate the attributes of design elements for graphic layout generation by forcing both the generator and the discriminator to meet attribute conditions. Due to the complexity of graphic designs, we further propose an element dropout method to make the discriminator look at partial lists of elements and learn their local patterns. In addition, we introduce various loss designs following different design principles for layout optimization. We demonstrate that the proposed method can synthesize graphic layouts conditioned on different element attributes. It can also adjust well-designed layouts to new sizes while retaining elements' original reading-orders. The effectiveness of our method is validated through a user study.

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“…The encapsulation of the energy function is performed by combining various image features (e.g., position) with layout features (e.g., alignment). Gonzalez-Morcillo et al [16] proposed an automatic design system based on expert knowledge. By optimising the layout features in the image through evolutionary algorithms, the system can realise the automatic layout of flat images.…”

Section: Introductionmentioning

confidence: 99%

Computational research on flat image design and style based on perceptual feature quantification

Wang

Zheng

2022

Applied Mathematics and Nonlinear Sciences

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In order to solve the problem that a large number of low-value and easy-to-consume graphic designs consume the human and financial resources of the society and also waste the inherent value of design resources, by dividing the layers of pixels, elements, relations, planes and applications, this article defines the corresponding feature representations and studies related methods for quantifying geometric features, perceptual features and style features. The content includes the extraction method of element colour, the calculation method of layout-aware feature and colour-match-aware feature, and the pairwise comparison method of style feature. The experimental results show that the method proposed in this article has lower complexity and is significantly better than other algorithms. Comparing the method proposed in this article with other methods, the average time of this algorithm is 2.6 s under the condition of guaranteed images. The study of the design feature-driven graphic image style calculation method is not only of great significance for solving repeated design problems but also provides a basis and reference for the future development of intelligent design.

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Gaudii: An Automated Graphic Design Expert System

Cited by 10 publications

References 10 publications

Learning Layouts for Single-PageGraphic Designs

Learning Layouts for Single-PageGraphic Designs

Attribute-Conditioned Layout GAN for Automatic Graphic Design

Computational research on flat image design and style based on perceptual feature quantification

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