Li Jianan, Yang Jimei, Zhang Jianming, Liu Chang, Wang Christina, Xu Tingfa
IEEE Trans Vis Comput Graph. 2021 Oct;27(10):4039-4048. doi: 10.1109/TVCG.2020.2999335. Epub 2021 Sep 1.
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 article, 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.
建模布局是平面设计重要的第一步。近来，生成图形布局的方法有了进展，尤其是借助生成对抗网络（GAN）。然而，指定设计元素的位置和大小的问题通常涉及到与元素属性相关的约束，比如面积、宽高比和阅读顺序。自动化属性条件图形布局仍然是一个复杂且未解决的问题。在本文中，我们引入属性条件布局GAN，通过迫使生成器和判别器都满足属性条件，将设计元素的属性纳入图形布局生成中。由于图形设计的复杂性，我们进一步提出一种元素丢弃方法，使判别器查看部分元素列表并学习其局部模式。此外，我们遵循不同的设计原则引入各种损失设计以进行布局优化。我们证明所提出的方法可以合成基于不同元素属性的图形布局。它还可以将设计良好的布局调整为新的尺寸，同时保留元素原来的阅读顺序。我们方法的有效性通过用户研究得到了验证。
