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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 The videos generated with tts are of higher quality and more consistent with the prompt than those generated without tts. 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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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. 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
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. Wan2.1 offers these key features:
