Sun, JiahuiZheng, LipingZhang, GaofengWu, WenmingChaine, RaphaëlleDeng, ZhigangKim, Min H.2023-10-092023-10-0920231467-8659https://doi.org/10.1111/cgf.14984https://diglib.eg.org:443/handle/10.1111/cgf14984Bubble 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.Keywords: Graph generation; Bubble diagram; Deep generative modeling CCS Concepts: Computing methodologies -> Shape modeling; Computer visionGraph generationBubble diagramDeep generative modeling CCS ConceptsComputing methodologiesShape modelingComputer vision10.1111/cgf.1498413 pages