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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 generate up to 50 fps videos at native 4k resolution with synchronized audio in one pass Learning united visual representation by alignment before projection if you like our project, please give us a star ⭐ on github for latest update
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 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.
It eliminates the need for heavyweight pretrained encoders and requires less than 50m parameters. Unlike previous models that serve as offline mode (querying/responding to a full video), our model supports online interaction within a video stream It can proactively update responses during a stream, such as recording activity changes or helping with the next steps in real time.
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