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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. Jan 21, 2025 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 Video-LLaVA: Learning United Visual Representation by Alignment Before Projection If you like our project, please give us a star on GitHub for latest update. I also have other video-language projects that may interest you . Open-Sora Plan: Open-Source Large Video Generation Model Check the YouTube videos resolution and the recommended speed needed to play the video. The table below shows the approximate speeds recommended to play each video resolution. Feb 23, 2025 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 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. Feb 25, 2025 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

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