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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.15749 |
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| _version_ | 1866915559732412416 |
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| author | Wang, Haoran Zhao, Bo Wang, Jinghui Wang, Hanzhang Yang, Huan Ji, Wei Liu, Hao Xiao, Xinyan |
| author_facet | Wang, Haoran Zhao, Bo Wang, Jinghui Wang, Hanzhang Yang, Huan Ji, Wei Liu, Hao Xiao, Xinyan |
| contents | In this paper, we study the content-aware layout generation problem, which aims to automatically generate layouts that are harmonious with a given background image. Existing methods usually deal with this task with a single-step reasoning framework. The lack of a feedback-based self-correction mechanism leads to their failure rates significantly increasing when faced with complex element layout planning. To address this challenge, we introduce SEGA, a novel Stepwise Evolution Paradigm for Content-Aware Layout Generation. Inspired by the systematic mode of human thinking, SEGA employs a hierarchical reasoning framework with a coarse-to-fine strategy: first, a coarse-level module roughly estimates the layout planning results; then, another refining module performs fine-level reasoning regarding the coarse planning results. Furthermore, we incorporate layout design principles as prior knowledge into the model to enhance its layout planning ability. Besides, we present GenPoster-100K that is a new large-scale poster dataset with rich meta-information annotation. The experiments demonstrate the effectiveness of our approach by achieving the state-of-the-art results on multiple benchmark datasets. Our project page is at: https://brucew91.github.io/SEGA.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15749 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SEGA: A Stepwise Evolution Paradigm for Content-Aware Layout Generation with Design Prior Wang, Haoran Zhao, Bo Wang, Jinghui Wang, Hanzhang Yang, Huan Ji, Wei Liu, Hao Xiao, Xinyan Computer Vision and Pattern Recognition In this paper, we study the content-aware layout generation problem, which aims to automatically generate layouts that are harmonious with a given background image. Existing methods usually deal with this task with a single-step reasoning framework. The lack of a feedback-based self-correction mechanism leads to their failure rates significantly increasing when faced with complex element layout planning. To address this challenge, we introduce SEGA, a novel Stepwise Evolution Paradigm for Content-Aware Layout Generation. Inspired by the systematic mode of human thinking, SEGA employs a hierarchical reasoning framework with a coarse-to-fine strategy: first, a coarse-level module roughly estimates the layout planning results; then, another refining module performs fine-level reasoning regarding the coarse planning results. Furthermore, we incorporate layout design principles as prior knowledge into the model to enhance its layout planning ability. Besides, we present GenPoster-100K that is a new large-scale poster dataset with rich meta-information annotation. The experiments demonstrate the effectiveness of our approach by achieving the state-of-the-art results on multiple benchmark datasets. Our project page is at: https://brucew91.github.io/SEGA.github.io/ |
| title | SEGA: A Stepwise Evolution Paradigm for Content-Aware Layout Generation with Design Prior |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.15749 |