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DepthAnything Video-Depth-Anything - GitHub 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-R1: Reinforcing Video Reasoning in MLLMs - GitHub Our Video-R1-7B obtain strong performance on several video reasoning benchmarks For example, Video-R1-7B attains a 35 8% accuracy on video spatial reasoning benchmark VSI-bench, surpassing the commercial proprietary model GPT-4o
Download the Google Meet app - Computer - Google Meet Help Accessories and hardware kits for Meet Set up Meet to help your team work remotely Accessibility in Google Meet Get the new Meet app in the play store or app store Google Meet is your one app for video calling and meetings across all devices Use video calling features like fun filters and effects or schedule time to connect when everyone can join
Troubleshoot YouTube video errors - Google Help 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
Generate videos with Gemini Apps - Computer - Google Help Generate videos with Gemini Apps You can create short videos in minutes in Gemini Apps with Veo 3 1, our latest AI video generator Simply describe what you have in mind and watch your ideas come to life in motion – whether you're creating for fun, sharing with friends, or visualizing a concept
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
GitHub - stepfun-ai Step-Video-T2V We present Step-Video-T2V, a state-of-the-art (SoTA) text-to-video pre-trained model with 30 billion parameters and the capability to generate videos up to 204 frames To enhance both training and inference efficiency, we propose a deep compression VAE for videos, achieving 16x16 spatial and 8x
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: 👍 SOTA Performance: Wan2 1 consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks 👍
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