BubbleFormer: Bubble Diagram Generation via Dual Transformer Models

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dc.contributor.authorSun, Jiahuien_US
dc.contributor.authorZheng, Lipingen_US
dc.contributor.authorZhang, Gaofengen_US
dc.contributor.authorWu, Wenmingen_US
dc.contributor.editorChaine, Raphaëlleen_US
dc.contributor.editorDeng, Zhigangen_US
dc.contributor.editorKim, Min H.en_US
dc.date.accessioned2023-10-09T07:38:33Z
dc.date.available2023-10-09T07:38:33Z
dc.date.issued2023
dc.description.abstractBubble diagrams serve as a crucial tool in the field of architectural planning and graphic design. With the surge of Artificial Intelligence Generated Content (AIGC), there has been a continuous emergence of research and development efforts focused on utilizing bubble diagrams for layout design and generation. However, there is a lack of research efforts focused on bubble diagram generation. In this paper, we propose a novel generative model, BubbleFormer, for generating diverse and plausible bubble diagrams. BubbleFormer consists of two improved Transformer networks: NodeFormer and EdgeFormer. These networks generate nodes and edges of the bubble diagram, respectively. To enhance the generation diversity, a VAE module is incorporated into BubbleFormer, allowing for the sampling and generation of numerous high-quality bubble diagrams. BubbleFormer is trained end-to-end and evaluated through qualitative and quantitative experiments. The results demonstrate that Bubble- Former can generate convincing and diverse bubble diagrams, which in turn drive downstream tasks to produce high-quality layout plans. The model also shows generalization capabilities in other layout generation tasks and outperforms state-of-the-art techniques in terms of quality and diversity. In previous work, bubble diagrams as input are provided by users, and as a result, our bubble diagram generative model fills a significant gap in automated layout generation driven by bubble diagrams, thereby enabling an end-to-end layout design and generation. Code for this paper is at https://github.com/cgjiahui/BubbleFormer.en_US
dc.description.number7
dc.description.sectionheadersColor Harmonization on Images
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume42
dc.identifier.doi10.1111/cgf.14984
dc.identifier.issn1467-8659
dc.identifier.pages13 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14984
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14984
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectKeywords: Graph generation; Bubble diagram; Deep generative modeling CCS Concepts: Computing methodologies -> Shape modeling; Computer vision
dc.subjectGraph generation
dc.subjectBubble diagram
dc.subjectDeep generative modeling CCS Concepts
dc.subjectComputing methodologies
dc.subjectShape modeling
dc.subjectComputer vision
dc.titleBubbleFormer: Bubble Diagram Generation via Dual Transformer Modelsen_US
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