@inproceedings{tang-etal-2025-slidecoder,
title = "{S}lide{C}oder: Layout-aware {RAG}-enhanced Hierarchical Slide Generation from Design",
author = "Tang, Wenxin and
Xiao, Jingyu and
Jiang, Wenxuan and
Xiao, Xi and
Wang, Yuhang and
Tang, Xuxin and
Li, Qing and
Ma, Yuehe and
Liu, Junliang and
Tang, Shisong and
Lyu, Michael R.",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.458/",
doi = "10.18653/v1/2025.emnlp-main.458",
pages = "9015--9039",
ISBN = "979-8-89176-332-6",
abstract = "Manual slide creation is labor-intensive and requires expert prior knowledge. Existing natural language-based LLM generation methods struggle to capture the visual and structural nuances of slide designs. To address this, we formalize the Reference Image to Slide Generation task and propose Slide2Code, the first benchmark with difficulty-tiered samples based on a novel Slide Complexity Metric. We introduce SlideCoder, a layout-aware, retrieval-augmented framework for generating editable slides from reference images. SlideCoder integrates a Color Gradient-based Segmentation algorithm and a Hierarchical Retrieval-Augmented Generation method to decompose complex tasks and enhance code generation. We also release SlideMaster, a 7B open-source model fine-tuned with improved reverse-engineered data. Experiments show that SlideCoder outperforms state-of-the-art baselines by up to 40.5 points, demonstrating strong performance across layout fidelity, execution accuracy, and visual consistency. Our code is available at https://github.com/vinsontang1/SlideCoder."
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<abstract>Manual slide creation is labor-intensive and requires expert prior knowledge. Existing natural language-based LLM generation methods struggle to capture the visual and structural nuances of slide designs. To address this, we formalize the Reference Image to Slide Generation task and propose Slide2Code, the first benchmark with difficulty-tiered samples based on a novel Slide Complexity Metric. We introduce SlideCoder, a layout-aware, retrieval-augmented framework for generating editable slides from reference images. SlideCoder integrates a Color Gradient-based Segmentation algorithm and a Hierarchical Retrieval-Augmented Generation method to decompose complex tasks and enhance code generation. We also release SlideMaster, a 7B open-source model fine-tuned with improved reverse-engineered data. Experiments show that SlideCoder outperforms state-of-the-art baselines by up to 40.5 points, demonstrating strong performance across layout fidelity, execution accuracy, and visual consistency. Our code is available at https://github.com/vinsontang1/SlideCoder.</abstract>
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%0 Conference Proceedings
%T SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design
%A Tang, Wenxin
%A Xiao, Jingyu
%A Jiang, Wenxuan
%A Xiao, Xi
%A Wang, Yuhang
%A Tang, Xuxin
%A Li, Qing
%A Ma, Yuehe
%A Liu, Junliang
%A Tang, Shisong
%A Lyu, Michael R.
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F tang-etal-2025-slidecoder
%X Manual slide creation is labor-intensive and requires expert prior knowledge. Existing natural language-based LLM generation methods struggle to capture the visual and structural nuances of slide designs. To address this, we formalize the Reference Image to Slide Generation task and propose Slide2Code, the first benchmark with difficulty-tiered samples based on a novel Slide Complexity Metric. We introduce SlideCoder, a layout-aware, retrieval-augmented framework for generating editable slides from reference images. SlideCoder integrates a Color Gradient-based Segmentation algorithm and a Hierarchical Retrieval-Augmented Generation method to decompose complex tasks and enhance code generation. We also release SlideMaster, a 7B open-source model fine-tuned with improved reverse-engineered data. Experiments show that SlideCoder outperforms state-of-the-art baselines by up to 40.5 points, demonstrating strong performance across layout fidelity, execution accuracy, and visual consistency. Our code is available at https://github.com/vinsontang1/SlideCoder.
%R 10.18653/v1/2025.emnlp-main.458
%U https://aclanthology.org/2025.emnlp-main.458/
%U https://doi.org/10.18653/v1/2025.emnlp-main.458
%P 9015-9039
Markdown (Informal)
[SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design](https://aclanthology.org/2025.emnlp-main.458/) (Tang et al., EMNLP 2025)
ACL
- Wenxin Tang, Jingyu Xiao, Wenxuan Jiang, Xi Xiao, Yuhang Wang, Xuxin Tang, Qing Li, Yuehe Ma, Junliang Liu, Shisong Tang, and Michael R. Lyu. 2025. SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 9015–9039, Suzhou, China. Association for Computational Linguistics.