  Content-aware visual-textual presentation layout aims at arranging spatial
space on the given canvas for pre-defined elements, including text, logo, and
underlay, which is a key to automatic template-free creative graphic design. In
practical applications, e.g., poster designs, the canvas is originally
non-empty, and both inter-element relationships as well as inter-layer
relationships should be concerned when generating a proper layout. A few recent
works deal with them simultaneously, but they still suffer from poor graphic
performance, such as a lack of layout variety or spatial non-alignment. Since
content-aware visual-textual presentation layout is a novel task, we first
construct a new dataset named PosterLayout, which consists of 9,974
poster-layout pairs and 905 images, i.e., non-empty canvases. It is more
challenging and useful for greater layout variety, domain diversity, and
content diversity. Then, we propose design sequence formation (DSF) that
reorganizes elements in layouts to imitate the design processes of human
designers, and a novel CNN-LSTM-based conditional generative adversarial
network (GAN) is presented to generate proper layouts. Specifically, the
discriminator is design-sequence-aware and will supervise the "design" process
of the generator. Experimental results verify the usefulness of the new
benchmark and the effectiveness of the proposed approach, which achieves the
best performance by generating suitable layouts for diverse canvases.
