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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:
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
GitHub - veo-3 veo-3 Veo 3 is Google DeepMind’s latest AI-powered video generation model, introduced at Google I O 2025 It enables users to create high-quality, 1080p videos from simple text or image prompts, integrating realistic audio elements such as dialogue, sound effects, and ambient noise
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
GitHub - Lightricks LTX-Video: Official repository for LTX-Video LTX-Video is the first DiT-based video generation model that can generate high-quality videos in real-time It can generate 30 FPS videos at 1216×704 resolution, faster than it takes to watch them The model is trained on a large-scale dataset of diverse videos and can generate high-resolution videos with realistic and diverse content The model supports image-to-video, keyframe-based
HunyuanVideo: A Systematic Framework For Large Video . . . - GitHub We present HunyuanVideo, a novel open-source video foundation model that exhibits performance in video generation that is comparable to, if not superior to, leading closed-source models In order to train HunyuanVideo model, we adopt several key technologies for model learning, including data
YouTube Help Learn more about YouTube YouTube help videos Browse our video library for helpful tips, feature overviews, and step-by-step tutorials YouTube Known Issues Get information on reported technical issues or scheduled maintenance
V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction . . . V-JEPA 2 Pre-training (Top) The encoder and predictor are pre-trained through self-supervised learning from video using a masked latent feature prediction objective, leveraging abundant natural videos to bootstrap physical world understanding and prediction (Bottom) Performance of V-JEPA 2 on downstream understanding and prediction tasks