Context-Aware Asset Search for Graphic Design

Kovács, Balázs; O’Donovan, Peter; Bala, Kavita; Hertzmann, Aaron

doi:10.1109/tvcg.2018.2842734

Cited by 13 publications

(8 citation statements)

References 42 publications

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“…The use of graphic elements in the computer field can abstract complex mathematical models into easy-to-understand digital graphics [6]. Using graphic elements in the design of digital media art interface can visualize information and achieve rapid information dissemination [7]. In addition, graphic elements are the most acceptable form of people's visual perception, so graphics are more and more used in interface design.…”

Section: Related Discoursementioning

confidence: 99%

Application and Analysis of Artificial Intelligence Graphic Element Algorithm in Digital Media Art Design

Tian

2022

Mobile Information Systems

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With the development of information technology, digital media art came into being. Digital media art interface design, as a window for direct communication with users, has a direct impact on the user’s experience, so it has attracted more and more people’s attention. In the context of the continuous development of artificial intelligence, artificial intelligence algorithms have penetrated into all fields of social development. This article is based on the integration of graphic elements with artificial intelligence algorithms to build a digital media art interface design system. According to the analysis of system requirements, combined with the algorithm of artificial intelligence graphic elements, the system hierarchy is constructed from the user model, window model, and display model; software development and driver development are realized through the media library, and the window and common controls are realized. On this basis, the creation and display of windows and common controls are realized, and the design of the core functions of the system is completed to ensure the effect of system functions. The final conclusion shows that in actual design applications, the algorithm in this paper has a very high probability of finding the optimal solution, about 90%, when the number of iterations is small. Adjust parameters α and β, when α = 0.6 and β = 0.4 , are the best parameters; the image reaches the highest PSNR value, and the image restoration quality is the best; after system testing, the running time of this system is shortened by an average of 1-2 seconds, and the target detection task accuracy rate is 100%; the system is evaluated by questionnaires. The overall satisfaction of the design interface remains at about 80%, which is a great improvement compared to the previous one. Finally, it satisfies the requirements of the digital media art interface design better, and it is worthy of further exploration.

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Section: Related Discoursementioning

confidence: 99%

Application and Analysis of Artificial Intelligence Graphic Element Algorithm in Digital Media Art Design

Tian

2022

Mobile Information Systems

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“…Automatic layout generation is a major point of interest in the field of graphic design [9], [10], [11], [12], [13]. Harrington et al [14] suggest an energy function to measure the aesthetics of a layout.…”

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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“…Existing product images are helpful to inspire designers to come out with new ideas and initial design schemes more quickly [158]. The image retrieval, match, and recommendation are key for visually inspire [3,159,160,161]. Designers and users put forward their requirements through images and text, search for related product images from databases or e-commerce websites, and the matched images will be recommended to designers and users as design references.…”

Section: Product Design Based On Image Datamentioning

confidence: 99%

Big Data driven Product Design: A Survey

Quan¹,

Li²,

Zeng³

et al. 2021

Preprint

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With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can no longer satisfy the market needs due to their strong subjectivity, small survey scope, poor real-time data, and lack of visual display, which calls for the development of big data driven product design methodology. Big data in the product lifecycle contains valuable information for guiding product design, such as customer preferences, market demands, product evaluation, and visual display: online product reviews reflect customer evaluations and requirements; product images contain information of shape,color, and texture which can inspire designers to get initial design schemes more quickly or even directly generate new product images. How to efficiently collect product design related data and exploit them effectively during the whole product design process is thus critical to modern product design. This paper aims to conduct a comprehensive survey on big data driven product design. It will help researchers and practitioners to comprehend the latest development of relevant studies and applications centered on how big data can be processed, analyzed, and exploited in aiding product design. We first introduce several representative traditional product design methods and highlight their limitations. Then we discuss current and potential applications of textual data, image data, audio data, and video data in product design cycles. Finally, major deficiencies of existing data driven product design studies and future research directions are summarized. We believe that this study can draw increasing attention to modern data driven product design.Keywords product design • big data • Kansei engineering • QFD • generative design * Correspondence.

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Context-Aware Asset Search for Graphic Design

Cited by 13 publications

References 42 publications

Application and Analysis of Artificial Intelligence Graphic Element Algorithm in Digital Media Art Design

Application and Analysis of Artificial Intelligence Graphic Element Algorithm in Digital Media Art Design

Attribute-Conditioned Layout GAN for Automatic Graphic Design

Big Data driven Product Design: A Survey

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