The Layout Generation Algorithm of Graphic Design Based on Transformer-CVAE
This addresses the problem of repetitive and inefficient layout design for professional graphic designers, though it appears incremental as it builds on existing models with new strategies.
The paper tackled the time-consuming task of manual layout design in graphic design by proposing LayoutT-CVAE, an end-to-end model based on Transformer and CVAE, which generated layouts that performed better on many metrics compared to existing state-of-the-art models.
Graphic design is ubiquitous in people's daily lives. For graphic design, the most time-consuming task is laying out various components in the interface. Repetitive manual layout design will waste a lot of time for professional graphic designers. Existing templates are usually rudimentary and not suitable for most designs, reducing efficiency and limiting creativity. This paper implemented the Transformer model and conditional variational autoencoder (CVAE) to the graphic design layout generation task. It proposed an end-to-end graphic design layout generation model named LayoutT-CVAE. We also proposed element disentanglement and feature-based disentanglement strategies and introduce new graphic design principles and similarity metrics into the model, which significantly increased the controllability and interpretability of the deep model. Compared with the existing state-of-art models, the layout generated by ours performs better on many metrics.