Constrained Graphic Layout Generation via Latent Optimization
This work addresses the need for flexible and user-controllable layout generation in graphic design, though it is incremental as it builds on existing models.
The paper tackles the problem of generating graphic layouts that incorporate design semantics, either implicitly or explicitly specified by users, by formulating layout generation as a constrained optimization problem in the latent space of an existing Transformer-based model. The result is a single model capable of generating realistic layouts in both constrained and unconstrained tasks, with code made publicly available.
It is common in graphic design humans visually arrange various elements according to their design intent and semantics. For example, a title text almost always appears on top of other elements in a document. In this work, we generate graphic layouts that can flexibly incorporate such design semantics, either specified implicitly or explicitly by a user. We optimize using the latent space of an off-the-shelf layout generation model, allowing our approach to be complementary to and used with existing layout generation models. Our approach builds on a generative layout model based on a Transformer architecture, and formulates the layout generation as a constrained optimization problem where design constraints are used for element alignment, overlap avoidance, or any other user-specified relationship. We show in the experiments that our approach is capable of generating realistic layouts in both constrained and unconstrained generation tasks with a single model. The code is available at https://github.com/ktrk115/const_layout .