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Bytedance †corresponding author 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 Added a preliminary chapter, reclassifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the llm background section. Notebooklm may take a while to generate the video overview, feel free to come back to your notebook later.
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This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the. Wan2.1 offers these key features: Learning united visual representation by alignment before projection if you like our project, please give us a star ⭐ on github for latest update
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The table below shows the approximate speeds recommended to play each video resolution. 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. Hack the valley ii, 2018
