PresentAgent: Multimodal Agent for Presentation Video Generation
We present PresentAgent, a multimodal agent that transforms long-form
documents into narrated presentation videos. While existing approaches are
limited to generating static slides or text summaries, our method advances
beyond these limitations by producing fully synchronized visual and spoken
content that closely mimics human-style presentations. To achieve this
integration, PresentAgent employs a modular pipeline that systematically
segments the input document, plans and renders slide-style visual frames,
generates contextual spoken narration with large language models and
Text-to-Speech models, and seamlessly composes the final video with precise
audio-visual alignment. Given the complexity of evaluating such multimodal
outputs, we introduce PresentEval, a unified assessment framework powered by
Vision-Language Models that comprehensively scores videos across three critical
dimensions: content fidelity, visual clarity, and audience comprehension
through prompt-based evaluation. Our experimental validation on a curated
dataset of 30 document-presentation pairs demonstrates that PresentAgent
approaches human-level quality across all evaluation metrics. These results
highlight the significant potential of controllable multimodal agents in
transforming static textual materials into dynamic, effective, and accessible
presentation formats. Code will be available at
https://github.com/AIGeeksGroup/PresentAgent.
686c92c5364e2ad167eb53fa
Jingwei Shi
claimed_verified
2025-07-29T12:51:19.839Z
64caef94dc4e838857cd756b
/avatars/d7982fc0a1ab8f4fd53272417fa88e05.svg
jingwei shi
user
sjw712
686c92c5364e2ad167eb53fb
Zeyu Zhang
claimed_verified
2025-07-08T08:06:20.306Z
64ec877bb93654d4ca5c92e9
https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
Zeyu Zhang
user
SteveZeyuZhang
686c92c5364e2ad167eb53fc
Biao Wu
686c92c5364e2ad167eb53fd
Yanjie Liang
686c92c5364e2ad167eb53fe
Meng Fang
686c92c5364e2ad167eb53ff
Ling Chen
686c92c5364e2ad167eb5400
Yang Zhao
PresentAgent: Multimodal Agent for Presentation Video Generation
We present PresentAgent, a multimodal agent that transforms long-form
documents into narrated presentation videos. While existing approaches are
limited to generating static slides or text summaries, our method advances
beyond these limitations by producing fully synchronized visual and spoken
content that closely mimics human-style presentations. To achieve this
integration, PresentAgent employs a modular pipeline that systematically
segments the input document, plans and renders slide-style visual frames,
generates contextual spoken narration with large language models and
Text-to-Speech models, and seamlessly composes the final video with precise
audio-visual alignment. Given the complexity of evaluating such multimodal
outputs, we introduce PresentEval, a unified assessment framework powered by
Vision-Language Models that comprehensively scores videos across three critical
dimensions: content fidelity, visual clarity, and audience comprehension
through prompt-based evaluation. Our experimental validation on a curated
dataset of 30 document-presentation pairs demonstrates that PresentAgent
approaches human-level quality across all evaluation metrics. These results
highlight the significant potential of controllable multimodal agents in
transforming static textual materials into dynamic, effective, and accessible
presentation formats. Code will be available at
https://github.com/AIGeeksGroup/PresentAgent.
PresentAgent generates high-quality narrated presentation videos from long-form documents using a modular pipeline and is evaluated using PresentEval, a Vision-Language Model-based framework.
multimodal agent PresentAgent document segmentation slide-style visual frames large language models Text-to-Speech models audio-visual alignment PresentEval Vision-Language Models content fidelity visual clarity audience comprehension prompt-based evaluation