Unifying Layout Generation with a Decoupled Diffusion Model
This work addresses the problem of reducing the burden on graphic design for formatted scenes like publications and UIs, offering a unified solution for layout generation, though it appears incremental in its method adaptation.
The paper tackles the challenge of unifying various layout generation subtasks, such as conditional and unconditional generation, by proposing a Layout Diffusion Generative Model (LDGM) that uses a decoupled diffusion approach. The result is a model that outperforms existing methods in both functionality and performance, as demonstrated through extensive experiments.
Layout generation aims to synthesize realistic graphic scenes consisting of elements with different attributes including category, size, position, and between-element relation. It is a crucial task for reducing the burden on heavy-duty graphic design works for formatted scenes, e.g., publications, documents, and user interfaces (UIs). Diverse application scenarios impose a big challenge in unifying various layout generation subtasks, including conditional and unconditional generation. In this paper, we propose a Layout Diffusion Generative Model (LDGM) to achieve such unification with a single decoupled diffusion model. LDGM views a layout of arbitrary missing or coarse element attributes as an intermediate diffusion status from a completed layout. Since different attributes have their individual semantics and characteristics, we propose to decouple the diffusion processes for them to improve the diversity of training samples and learn the reverse process jointly to exploit global-scope contexts for facilitating generation. As a result, our LDGM can generate layouts either from scratch or conditional on arbitrary available attributes. Extensive qualitative and quantitative experiments demonstrate our proposed LDGM outperforms existing layout generation models in both functionality and performance.