Aesthetic Text Logo Synthesis via Content-aware Layout Inferring
This addresses the labor-intensive task of text logo design for designers, offering an automated solution, though it appears incremental as it builds on existing layout generation methods.
The paper tackles the problem of automating aesthetic text logo design by proposing a content-aware layout generation network that synthesizes layouts from glyph images and text, achieving visually-pleasing results as demonstrated on a new dataset of 3,500 images.
Text logo design heavily relies on the creativity and expertise of professional designers, in which arranging element layouts is one of the most important procedures. However, few attention has been paid to this task which needs to take many factors (e.g., fonts, linguistics, topics, etc.) into consideration. In this paper, we propose a content-aware layout generation network which takes glyph images and their corresponding text as input and synthesizes aesthetic layouts for them automatically. Specifically, we develop a dual-discriminator module, including a sequence discriminator and an image discriminator, to evaluate both the character placing trajectories and rendered shapes of synthesized text logos, respectively. Furthermore, we fuse the information of linguistics from texts and visual semantics from glyphs to guide layout prediction, which both play important roles in professional layout design. To train and evaluate our approach, we construct a dataset named as TextLogo3K, consisting of about 3,500 text logo images and their pixel-level annotations. Experimental studies on this dataset demonstrate the effectiveness of our approach for synthesizing visually-pleasing text logos and verify its superiority against the state of the art.