Jan 21, 2025 · 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. Run an Internet speed test to make sure that 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 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 Jun 3, 2024 · 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. 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. Feb 23, 2025 · Inspired by DeepSeek-R1's success in eliciting reasoning abilities through rule-based RL, we introduce Video-R1 as the first work to systematically explore the R1 paradigm for eliciting video reasoning within MLLMs. A machine learning-based video super resolution and frame interpolation framework.
Est. Hack the Valley II, 2018. - k4yt3x/video2x 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-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 Video.js is a free and open source library, and we appreciate any help you're willing to give - whether it's fixing bugs, improving documentation, or suggesting new features.