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DepthAnything Video-Depth-Anything - GitHub 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 Compared with other diffusion-based models, it enjoys faster inference speed, fewer parameters, and higher consistent depth
GitHub - MME-Benchmarks Video-MME: [CVPR 2025] Video-MME: The First . . . We introduce Video-MME, the first-ever full-spectrum, M ulti- M odal E valuation benchmark of MLLMs in Video analysis 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
Troubleshoot YouTube video errors - Google Help Run an internet speed test to make sure your internet can support the selected video resolution Using multiple devices on the same network may reduce the speed that your device gets You can also change the quality of your video to improve your experience Check the YouTube video’s resolution and the recommended speed needed to play the video The table below shows the approximate speeds
Wan: Open and Advanced Large-Scale Video Generative Models Wan: Open and Advanced Large-Scale Video Generative Models In this repository, we present Wan2 1, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation Wan2 1 offers these key features:
Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub Video-R1 significantly outperforms previous models across most benchmarks Notably, on VSI-Bench, which focuses on spatial reasoning in videos, Video-R1-7B achieves a new state-of-the-art accuracy of 35 8%, surpassing GPT-4o, a proprietary model, while using only 32 frames and 7B parameters This highlights the necessity of explicit reasoning capability in solving video tasks, and confirms the
VideoLLM-online: Online Video Large Language Model for Streaming Video Online Video Streaming: 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
Generate Video Overviews in NotebookLM - Google Help Video Overviews, including voices and visuals, are AI-generated and may contain inaccuracies or audio glitches NotebookLM may take a while to generate the Video Overview, feel free to come back to your notebook later