Layout Generation: Automated Components Placement for Advertising Poster using Transformer-Based from Layout Graph

In the digital era, graphic design plays an important role in a company's marketing strategy, especially advertising posters that can convey messages to the audience. However, the process of making attractive and informative posters takes a long time, especially in the placement of components in the layout. This research aims to develop a layout generator system that focuses on placing components on the layout using the SGTransformer method. SGTransformer is one of the transformer-based methods that can be used to generate layouts. This research utilizes the capabilities of Laplacian positional encoding (LapPE) and edge features in SGTransformer that can improve the model's performance in developing a more structured layout based on the input layout graph. A layout graph describes the spatial relationship between components in a layout. The SGTransformer model is trained using advertising poster datasets collected from social media. Once trained, the model automatically places components based on the description of the spatial relationship between components represented by the layout graph. Evaluation of the model shows that the SGTransformer method can produce structured and more diverse layouts although there are still challenges such as overlap between components. This research contributes to the efficiency of the advertising poster design process and provides practical solutions to the creation of advertising posters in the digital marketing industry. Code and other materials will be released at https://github.com/syahdeee/Layout-Generator.