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This work presents video depth anything based on depth anything v2, which can be applied to arbitrarily long videos without compromising quality, consistency, or generalization ability It can proactively update responses during a stream, such as recording activity changes or helping with the next steps in real time. It is designed to comprehensively assess the capabilities of mllms in processing video data, covering a wide range of visual domains, temporal durations, and data modalities.

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This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the. Notebooklm may take a while to generate the video overview, feel free to come back to your notebook later. Added a preliminary chapter, reclassifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the llm background section.