- DepthAnything Video-Depth-Anything - GitHub
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
- 【EMNLP 2024 】Video-LLaVA: Learning United Visual . . . - GitHub
😮 Highlights Video-LLaVA exhibits remarkable interactive capabilities between images and videos, despite the absence of image-video pairs in the dataset
- 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
- Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video . . .
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
- GitHub - k4yt3x video2x: A machine learning-based video super . . .
A machine learning-based video super resolution and frame interpolation framework Est Hack the Valley II, 2018 - k4yt3x video2x
- Troubleshoot YouTube video errors - Google Help
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
- 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:
- Video-R1: Reinforcing Video Reasoning in MLLMs - GitHub
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
|