Personalization as Inverse Planning: Learning Latent Design Intents for Agentic Slide Generation via Structural Denoising
arXiv:2607.00407v1 Announce Type: new Abstract: Slide design requires personalizing both deck themes and page layouts. Yet, current AI agent-based methods struggle with fine-grained, page-level design. Solely relying on prespecified templates or user verbose instructions, they fail to capture latent design intents, leaving Page-level Slide Personalization (PSP) unresolved. To close this gap, this work formulates PSP as an inverse planning problem. We propose to learn a design intent without assuming any knowledge of the specific executing tools (e.g., PowerPoint, Beamer) being used. However, r
The proliferation of AI-driven content generation tools highlights the urgent need for more sophisticated, personalized design capabilities, moving beyond template-based approaches.
This breakthrough advances personalized content creation by allowing AI to understand and execute nuanced design intent, significantly improving the quality and relevance of AI-generated materials.
AI-generated presentations and visual content can now be tailored to a far greater degree, reflecting latent user preferences rather than just explicit instructions or generic templates.
- · AI design tool developers
- · Creative agencies leveraging AI
- · Businesses requiring personalized content at scale
- · End-users of presentation software
- · Generic template providers
- · Manual presentation designers focused on basic layouts
Increased adoption of AI for complex personalized content generation, reducing human effort in design.
New business models emerging around AI-driven personalized design services and platforms.
The blurring of lines between manual design and AI-generated design, integrating AI more deeply into creative workflows.
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Read at arXiv cs.AI