title: MM-VID: Advancing Video Understanding with GPT-4V(ision)
summary: We present MM-VID, an integrated system that harnesses the capabilities of
  GPT-4V, combined with specialized tools in vision, audio, and speech, to
  facilitate advanced video understanding. MM-VID is designed to address the
  challenges posed by long-form videos and intricate tasks such as reasoning
  within hour-long content and grasping storylines spanning multiple episodes.
  MM-VID uses a video-to-script generation with GPT-4V to transcribe multimodal
  elements into a long textual script. The generated script details character
  movements, actions, expressions, and dialogues, paving the way for large
  language models (LLMs) to achieve video understanding. This enables advanced
  capabilities, including audio description, character identification, and
  multimodal high-level comprehension. Experimental results demonstrate the
  effectiveness of MM-VID in handling distinct video genres with various video
  lengths. Additionally, we showcase its potential when applied to interactive
  environments, such as video games and graphic user interfaces.
paper_authors._id: 654088b86e8724317845f3dc
paper_authors.name: Kevin Lin
paper_authors.status: claimed_verified
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paper_authors.fullname: Kevin Lin
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paper_authors.user: kevinlin311tw
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paper_authors.name: Faisal Ahmed
paper_authors._id: 654088b86e8724317845f3de
paper_authors.name: Linjie Li
paper_authors.status: admin_assigned
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paper_authors.fullname: Linjie Li
paper_authors.type: user
paper_authors.user: linjieli222
paper_authors._id: 654088b86e8724317845f3df
paper_authors.name: Chung-Ching Lin
paper_authors.status: claimed_verified
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paper_authors.fullname: CC Lin
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paper_authors.user: cclin10
paper_authors._id: 654088b86e8724317845f3e0
paper_authors.name: Ehsan Azarnasab
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paper_authors.fullname: Ehsan Azarnasab
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paper_authors.user: dashesy
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paper_authors.name: Zhengyuan Yang
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paper_authors.avatarUrl: /avatars/fb36f69f03421c3a2a7f72ba0858fa60.svg
paper_authors.fullname: Zhengyuan Yang
paper_authors.type: user
paper_authors.user: zyang39
paper_authors._id: 654088b86e8724317845f3e2
paper_authors.name: Jianfeng Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-31T08:30:31.121Z
paper_authors._id: 641404e74e5305c14f269b3c
paper_authors.avatarUrl: /avatars/0dbe862cde7e4bef25bedf860584d8c1.svg
paper_authors.fullname: Jianfeng Wang
paper_authors.type: user
paper_authors.user: amsword8
paper_authors._id: 654088b86e8724317845f3e3
paper_authors.name: Lin Liang
paper_authors._id: 654088b86e8724317845f3e4
paper_authors.name: Zicheng Liu
paper_authors._id: 654088b86e8724317845f3e5
paper_authors.name: Yumao Lu
paper_authors._id: 654088b86e8724317845f3e6
paper_authors.name: Ce Liu
paper_authors._id: 654088b86e8724317845f3e7
paper_authors.name: Lijuan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-31T08:31:40.453Z
paper_authors._id: 6413521d4e5305c14f22e110
paper_authors.avatarUrl: /avatars/a6f8d0573e678f79bc3c0b7897b818ce.svg
paper_authors.fullname: Lijuan Wang
paper_authors.type: user
paper_authors.user: Lijuan
paper_title: MM-VID: Advancing Video Understanding with GPT-4V(ision)
paper_summary: We present MM-VID, an integrated system that harnesses the capabilities of
  GPT-4V, combined with specialized tools in vision, audio, and speech, to
  facilitate advanced video understanding. MM-VID is designed to address the
  challenges posed by long-form videos and intricate tasks such as reasoning
  within hour-long content and grasping storylines spanning multiple episodes.
  MM-VID uses a video-to-script generation with GPT-4V to transcribe multimodal
  elements into a long textual script. The generated script details character
  movements, actions, expressions, and dialogues, paving the way for large
  language models (LLMs) to achieve video understanding. This enables advanced
  capabilities, including audio description, character identification, and
  multimodal high-level comprehension. Experimental results demonstrate the
  effectiveness of MM-VID in handling distinct video genres with various video
  lengths. Additionally, we showcase its potential when applied to interactive
  environments, such as video games and graphic user interfaces.
paper_ai_keywords: MM-VID GPT-4V video-to-script generation multimodal elements long textual script character movements actions expressions dialogues large language models (LLMs) audio description character identification multimodal high-level comprehension