无需模板，用分层流程自动生成美观幻灯片。
Design First, Code Later: Aesthetically Pleasing Template-Free Slides Generation
- 分层设计流程分离布局构思与代码实现，摆脱模板束缚。
- 在人工评估中优于基线方法，用户偏好度显著提升。
- 适合需要创意排版的自动化演示生成场景。
自动制作演示幻灯片需在严格空间约束下协调叙事结构与页面视觉设计。现有方法依赖固定模板或直接生成可执行代码，既限制了大模型的创意布局能力，又跳过了关键的设计环节。本文提出一种分层幻灯片生成流程 DeepSlides，无需预设模板或风格，将页面设计与实现解耦；构建专用于幻灯片生成的 SlideDesign 数据集；并采用多智能体强化学习训练框架，推出 SlideQwens 模型完成设计与实现。实验表明，该框架在多项指标上超越基线方法，在人工偏好评估中表现更优。数据集与代码已开源：https://github.com/sxswz213/DeepSlides。
原文摘要 · Abstract (English)
Producing presentation slides automatically entails coordinating narrative structure with page-level graphic design under strict spatial constraints. For such structured multimodal tasks, a well-organized design process is essential to ensure the final quality of slides. Existing approaches rely on fixed templates or directly emit executable code, thereby both limiting the creative layout-design capabilities of LLMs and bypassing the essential slide-page design step. To address these limitations, this paper (1) proposes a hierarchical slides generation workflow, DeepSlides, that systematically organizes slide design tasks without any predefined template or style, decoupling slide-page design from implementation; (2) introduces SlideDesign, a dataset tailored specifically for slides generation tasks; and (3) presents a multi-agent reinforcement learning training paradigm and trains a couple of models, SlideQwens, for slide design and implementation. Experimental results demonstrate that our proposed framework outperforms baseline methods on evaluated metrics and achieves superior performance in human preference evaluations. The dataset and code are available at https://github.com/sxswz213/DeepSlides.
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