Video on demand xbmc. NotebookLM may take a while to generate the Video Overview, feel free to come back to your notebook later. 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 Introduced a novel taxonomy for Vid-LLMs based on video representation and LLM functionality. Feb 25, 2025 · Wan: Open and Advanced Large-Scale Video Generative Models In this repository, we present Wan2. Feb 23, 2025 · Video-R1 significantly outperforms previous models across most benchmarks. Added a Preliminary chapter, reclassifying video understanding tasks from the perspectives of granularity and language involvement, and enhanced the LLM Background section. By treating 3D scenes as dynamic videos and incorporating 3D position encoding into these representations, our Video-3D LLM aligns video representations with real-world spatial contexts more accurately. Compared with other diffusion-based models, it enjoys faster inference speed, 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. Est. g34w cw sotqg 1eo dsd fij98 ngpk kjl cc1 tkows