Neural Design Network: Graphic Layout Generation with Constraints
This addresses the need for automated graphic design layout generation with constraints, which is incremental as it builds on existing layout synthesis methods.
The paper tackles the problem of generating graphic layouts that satisfy user-specified constraints, proposing a neural design network (NDN) that predicts relations, generates layouts, and fine-tunes them, with results showing visual similarity to real designs and practical application in layout recommendation.
Graphic design is essential for visual communication with layouts being fundamental to composing attractive designs. Layout generation differs from pixel-level image synthesis and is unique in terms of the requirement of mutual relations among the desired components. We propose a method for design layout generation that can satisfy user-specified constraints. The proposed neural design network (NDN) consists of three modules. The first module predicts a graph with complete relations from a graph with user-specified relations. The second module generates a layout from the predicted graph. Finally, the third module fine-tunes the predicted layout. Quantitative and qualitative experiments demonstrate that the generated layouts are visually similar to real design layouts. We also construct real designs based on predicted layouts for a better understanding of the visual quality. Finally, we demonstrate a practical application on layout recommendation.