  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.
