  In this paper, we introduce a novel generative model, Diffusion Layout
Transformers without Autoencoder (Dolfin), which significantly improves the
modeling capability with reduced complexity compared to existing methods.
Dolfin employs a Transformer-based diffusion process to model layout
generation. In addition to an efficient bi-directional (non-causal joint)
sequence representation, we further propose an autoregressive diffusion model
(Dolfin-AR) that is especially adept at capturing rich semantic correlations
for the neighboring objects, such as alignment, size, and overlap. When
evaluated against standard generative layout benchmarks, Dolfin notably
improves performance across various metrics (fid, alignment, overlap, MaxIoU
and DocSim scores), enhancing transparency and interoperability in the process.
Moreover, Dolfin's applications extend beyond layout generation, making it
suitable for modeling geometric structures, such as line segments. Our
experiments present both qualitative and quantitative results to demonstrate
the advantages of Dolfin.

Computer Vision and Pattern Recognition;Machine Learning