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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 Added a preliminary chapter, reclassifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the llm background section. Hack the valley ii, 2018
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Learning united visual representation by alignment before projection if you like our project, please give us a star ⭐ on github for latest update 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.
Wan2.1 offers these key features:
Unlike previous models that serve as offline mode (querying/responding to a full video), our model supports online interaction within a video stream
