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 accuracy.
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.
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
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.
Check the YouTube video’s resolution and the recommended speed needed to play the video. The table below shows the approximate speeds recommended to play each video resolution.
Create a video using help me create You can use help me create to generate a first-draft video with Gemini in Google Vids. All you need to do is enter a description. Gemini then generates a draft—including a script, AI voiceover, scenes, and content—for the video. You can then edit the draft as needed. On your computer, open Google Vids.
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.
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 ...
Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding This is the repo for the Video-LLaMA project, which is working on empowering large language models with video and audio understanding capabilities.
