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Computer Science > Computer Vision and Pattern Recognition

arXiv:2510.05096 (cs)
[Submitted on 6 Oct 2025 (v1), last revised 9 Oct 2025 (this version,
v2)]

Title:Paper2Video: Automatic Video Generation from Scientific Papers

Authors:Zeyu Zhu, Kevin Qinghong Lin, Mike Zheng Shou
View a PDF of the paper titled Paper2Video: Automatic Video
Generation from Scientific Papers, by Zeyu Zhu and 2 other authors
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    Abstract:Academic presentation videos have become an essential
    medium for research communication, yet producing them remains
    highly labor-intensive, often requiring hours of slide design,
    recording, and editing for a short 2 to 10 minutes video. Unlike
    natural video, presentation video generation involves distinctive
    challenges: inputs from research papers, dense multi-modal
    information (text, figures, tables), and the need to coordinate
    multiple aligned channels such as slides, subtitles, speech, and
    human talker. To address these challenges, we introduce
    Paper2Video, the first benchmark of 101 research papers paired
    with author-created presentation videos, slides, and speaker
    metadata. We further design four tailored evaluation
    metrics--Meta Similarity, PresentArena, PresentQuiz, and IP
    Memory--to measure how videos convey the paper's information to
    the audience. Building on this foundation, we propose
    PaperTalker, the first multi-agent framework for academic
    presentation video generation. It integrates slide generation
    with effective layout refinement by a novel effective tree search
    visual choice, cursor grounding, subtitling, speech synthesis,
    and talking-head rendering, while parallelizing slide-wise
    generation for efficiency. Experiments on Paper2Video demonstrate
    that the presentation videos produced by our approach are more
    faithful and informative than existing baselines, establishing a
    practical step toward automated and ready-to-use academic video
    generation. Our dataset, agent, and code are available at this
    https URL.

Comments: Project Page: this https URL
          Computer Vision and Pattern Recognition (cs.CV); Artificial
Subjects: Intelligence (cs.AI); Computation and Language (cs.CL);
          Multiagent Systems (cs.MA); Multimedia (cs.MM)
Cite as:  arXiv:2510.05096 [cs.CV]
          (or arXiv:2510.05096v2 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2510.05096
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Zeyu Zhu Mr. [view email]
[v1] Mon, 6 Oct 2025 17:58:02 UTC (6,815 KB)
[v2] Thu, 9 Oct 2025 17:29:00 UTC (6,817 KB)
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