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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 difsion-based models, it enjoys faster inference sd, 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 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%, surping 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 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 ll-spectrum, M ulti- M odal E valuation benchmark of MLLMs in Video ysis. It is designed to comprehensively ess 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 features: Check the YouTube video’s resolution and the recommended sd needed to play the video. The table below shows the approximate sds recommended to play each video resolution. 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. Introduced a novel taxonomy for Vid-LLMs based on video representation and LLM nctionality. Added a Preliminary chapter, reclifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the LLM Background section. Video Overviews, including voices and visuals, are

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