Browsing by Author "Wu, Wenming"

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    BubbleFormer: Bubble Diagram Generation via Dual Transformer Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Sun, Jiahui; Zheng, Liping; Zhang, Gaofeng; Wu, Wenming; Chaine, Raphaƫlle; Deng, Zhigang; Kim, Min H.
    Bubble 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.
  • Thumbnail Image
    Item
    PowerRTF: Power Diagram based Restricted Tangent Face for Surface Remeshing
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Yao, Yuyou; Liu, Jingjing; Fei, Yue; Wu, Wenming; Zhang, Gaofeng; Yan, Dong-Ming; Zheng, Liping; Memari, Pooran; Solomon, Justin
    Triangular meshes of superior quality are important for geometric processing in practical applications. Existing approximative CVT-based remeshing methodology uses planar polygonal facets to fit the original surface, simplifying the computational complexity. However, they usually do not consider surface curvature. Topological errors and outliers can also occur in the close sheet surface remeshing, resulting in wrong meshes. With this regard, we present a novel method named PowerRTF, an extension of the restricted tangent face (RTF) in conjunction with the power diagram, to better approximate the original surface with curvature adaption. The idea is to introduce a weight property to each sample point and compute the power diagram on the tangent face to produce area-controlled polygonal facets. Based on this, we impose the variable-capacity constraint and centroid constraint to the PowerRTF, providing the trade-off between mesh quality and computational efficiency. Moreover, we apply a normal verification-based inverse side point culling method to address the topological errors and outliers in close sheet surface remeshing. Our method independently computes and optimizes the PowerRTF per sample point, which is efficiently implemented in parallel on the GPU. Experimental results demonstrate the effectiveness, flexibility, and efficiency of our method.