OmniLayout: Enabling Coarse-to-Fine Learning with LLMs for Universal
  Document Layout Generation
Authors: name: Hengrui Kang, name: Zhuangcheng Gu, name: Zhiyuan Zhao, name: Zichen Wen, name: Bin Wang, name: Weijia Li, name: Conghui He

Document AI has advanced rapidly and is attracting increasing attention. Yet,
while most efforts have focused on document layout analysis (DLA), its
generative counterpart, document layout generation, remains underexplored. A
major obstacle lies in the scarcity of diverse layouts: academic papers with
Manhattan-style structures dominate existing studies, while open-world genres
such as newspapers and magazines remain severely underrepresented. To address
this gap, we curate OmniLayout-1M, the first million-scale dataset of diverse
document layouts, covering six common document types and comprising
contemporary layouts collected from multiple sources. Moreover, since existing
methods struggle in complex domains and often fail to arrange long sequences
coherently, we introduce OmniLayout-LLM, a 0.5B model with designed two-stage
Coarse-to-Fine learning paradigm: 1) learning universal layout principles from
OmniLayout-1M with coarse category definitions, and 2) transferring the
knowledge to a specific domain with fine-grained annotations. Extensive
experiments demonstrate that our approach achieves strong performance on
multiple domains in M^{6}Doc dataset, substantially surpassing both existing
layout generation experts and several latest general-purpose LLMs. Our code,
models, and dataset will be publicly released.