MM-VID: Advancing Video Understanding with GPT-4V(ision)
Kevin Lin Faisal Ahmed Linjie Li Chung-Ching Lin Ehsan Azarnasab Zhengyuan Yang Jianfeng Wang Lin Liang Zicheng Liu Yumao Lu Ce Liu Lijuan Wang
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.