OmniLayout: Enabling Coarse-to-Fine Learning with LLMs for Universal
  Document Layout Generation
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.
6904468e2c556835fa67f131
Hengrui Kang
claimed_verified
2025-10-31T14:26:34.738Z
66c598b536a75deef8bb21c0
/avatars/b3721202d01d3c947c36f0beae43ea1c.svg
Hengrui Kang
khr0516
user
khr0516
6904468e2c556835fa67f132
Zhuangcheng Gu
6904468e2c556835fa67f133
Zhiyuan Zhao
6904468e2c556835fa67f134
Zichen Wen
claimed_verified
2026-02-03T10:10:12.677Z
653b8c3e97a4d71d950e2f20
/avatars/b68880022e14556d0be58c69615db3be.svg
Zichen Wen
zichenwen
user
zichenwen
6904468e2c556835fa67f135
Bin Wang
6904468e2c556835fa67f136
Weijia Li
6904468e2c556835fa67f137
Conghui He
OmniLayout: Enabling Coarse-to-Fine Learning with LLMs for Universal
  Document Layout Generation
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.
A new dataset and model for document layout generation address the scarcity of diverse layouts and improve performance across multiple domains.
document layout analysis document layout generation OmniLayout-1M OmniLayout-LLM Coarse-to-Fine learning M$^{6}$Doc dataset