OmniPresent: Generating Coherent Presentation Suites from Scientific Papers
- URL: http://arxiv.org/abs/2607.02590v1
- Date: Wed, 01 Jul 2026 07:01:40 GMT
- Title: OmniPresent: Generating Coherent Presentation Suites from Scientific Papers
- Abstract summary: We formalize the task of unified presentation suite generation and propose $textbf OmniPresent$ to orchestrate the creation of coherent deliverables.<n>Our framework adopts a renderable HTML representation to enable centralized content planning and a self-correcting verify-and-repair loop that actively resolves conflicts across modalities.<n> Empirical results confirm that our method generates high-quality and faithful presentation suites that significantly surpass strong baselines in both accuracy and visual appeal.
- Score: 23.902743401868708
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Transforming static research papers into dynamic media such as posters, slides, and videos is essential for effective dissemination but remains a labor-intensive challenge. Existing automated approaches often treat these formats in isolation and consequently fail to maintain semantic consistency across the entire presentation suite. We address this fragmentation by formalizing the task of unified presentation suite generation and proposing $\textbf{OmniPresent}$ to orchestrate the creation of coherent deliverables. Our framework adopts a renderable HTML representation to enable centralized content planning and a self-correcting verify-and-repair loop that actively resolves conflicts across modalities. We further facilitate scalable research in this domain by releasing $\textbf{OmniPreBench}$, a comprehensive dataset comprising over one thousand papers with paired artifacts, and establishing a rigorous VLM-based evaluation protocol. Empirical results confirm that our method generates high-quality and faithful presentation suites that significantly surpass strong baselines in both accuracy and visual appeal.
Related papers
- Achieving Text-based Person Retrieval with Any Granularity [80.69427598925718]
This paper introduces a new paradigm, Text-based Person Retrieval with Any Granularity.<n>We formalize a five-level granularity spectrum and construct UFine6926-MG, a high-quality multi-grained annotated dataset.<n>We then propose the Cross-modal Multi-grained Aligning and Matching framework.
arXiv Detail & Related papers (2026-07-23T08:42:25Z) - Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation [74.0621258662676]
We propose Ptah, a multi-agent harness for interleaved report generation.<n>Ptah orchestrates the lifecycle from user query to rendered web report through planning, research, and writing stages.<n>A verifier agent serves as the harness's acceptance function, enforcing factual grounding, citation fidelity, and cross-modal consistency throughout the workflow.
arXiv Detail & Related papers (2026-05-28T12:40:34Z) - DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding [63.257540233507626]
We propose a paradigm that requires the model to execute a structured Analysis, Localization and Reasoning workflow.<n>We show it robustly generalizes from short-page training to ultra-long documents and is naturally synergistic with visual Retrieval-Augmented Generation systems.
arXiv Detail & Related papers (2026-04-14T14:39:26Z) - Towards Real-World Document Parsing via Realistic Scene Synthesis and Document-Aware Training [23.87978106727431]
We propose a data-training co-design framework for robust end-to-end document parsing.<n>Our method achieves superior accuracy and robustness across both scanned/digital and real-world captured scenarios.
arXiv Detail & Related papers (2026-03-25T03:19:09Z) - Fine-tuning MLLMs Without Forgetting Is Easier Than You Think [72.59321247529975]
We design a 2x2 experimental framework to assess model performance across in-distribution and out-of-distribution image and text inputs.<n>Our results show that appropriate regularization, such as constraining the number of trainable parameters or adopting a low learning rate, effectively prevents forgetting when dealing with out-of-distribution images.<n>We attribute this forgetting as task-specific overfitting and address this issue by introducing a data-hybrid training strategy.
arXiv Detail & Related papers (2026-03-15T17:16:19Z) - MURE: Hierarchical Multi-Resolution Encoding via Vision-Language Models for Visual Document Retrieval [111.99106496142474]
Visual Document Retrieval (VDR) requires representations that capture both fine-grained visual details and global document structure.<n>Existing VDR models struggle to balance effectiveness and efficiency when processing high-resolution documents.<n>We propose a new X-VisEmb paradigm that progresses from multi-resolution sampling and encoding, through cross-granularity feature fusion, to adaptive representation distillation.
arXiv Detail & Related papers (2026-03-07T15:17:22Z) - PaperX: A Unified Framework for Multimodal Academic Presentation Generation with Scholar DAG [22.64989700220684]
We introduce PaperX, a unified framework that models academic presentation generation as a structural transformation and rendering process.<n>PaperX generates diverse, high quality outputs from a single source.
arXiv Detail & Related papers (2026-01-30T18:27:03Z) - FocalOrder: Focal Preference Optimization for Reading Order Detection [23.497081928689525]
We propose textbfFocalOrder, a framework driven by textbfFocal Preference Optimization (FPO).<n>FocalOrder employs adaptive difficulty discovery with exponential moving average mechanism to dynamically pinpoint hard-to-learn transitions.<n>Experiments demonstrate that FocalOrder establishes new state-of-the-art results on OmniDocBench v1.0 and Comp-HRDoc.
arXiv Detail & Related papers (2026-01-12T12:37:04Z) - Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
arXiv Detail & Related papers (2025-10-17T02:33:16Z) - Multimodal Fact Checking with Unified Visual, Textual, and Contextual Representations [2.139909491081949]
We propose a unified framework for fine-grained multimodal fact verification called "MultiCheck"<n>Our architecture combines dedicated encoders for text and images with a fusion module that captures cross-modal relationships using element-wise interactions.<n>We evaluate our approach on the Factify 2 dataset, achieving a weighted F1 score of 0.84, substantially outperforming the baseline.
arXiv Detail & Related papers (2025-08-07T07:36:53Z) - DREAM: Document Reconstruction via End-to-end Autoregressive Model [53.51754520966657]
We present an innovative autoregressive model specifically designed for document reconstruction, referred to as Document Reconstruction via End-to-end Autoregressive Model (DREAM)<n>We establish a standardized definition of the document reconstruction task, and introduce a novel Document Similarity Metric (DSM) and DocRec1K dataset for assessing the performance of the task.
arXiv Detail & Related papers (2025-07-08T09:24:07Z) - PresentAgent: Multimodal Agent for Presentation Video Generation [30.274831875701217]
We present PresentAgent, a multimodal agent that transforms long-form documents into narrated presentation videos.<n>To achieve this integration, PresentAgent employs a modular pipeline that segments the input document, plans and renders slide-style visual frames.<n>Given the complexity of evaluating such multimodal outputs, we introduce PresentEval, a unified assessment framework powered by Vision-Language Models.
arXiv Detail & Related papers (2025-07-05T13:24:15Z) - DOC2PPT: Automatic Presentation Slides Generation from Scientific
Documents [76.19748112897177]
We present a novel task and approach for document-to-slide generation.
We propose a hierarchical sequence-to-sequence approach to tackle our task in an end-to-end manner.
Our approach exploits the inherent structures within documents and slides and incorporates paraphrasing and layout prediction modules to generate slides.
arXiv Detail & Related papers (2021-01-28T03:21:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.