What is LongCat-Video
LongCat-Video is a 13.6-billion-parameter model open-sourced by Meituan's LongCat team,AI video generationmodel, intext-to-video（Text-to-Video）、image-to-video(Image-to-Video) and video continuation (Video-Continuation) and other tasks, it performs excellently, and is especially good at efficiently generating high-quality long videos. Through multi-reward reinforcement learning optimization (GRPO), the model demonstrates performance comparable to leading open-source video generation models and the latest commercial solutions on internal and public benchmarks.
Main Features of LongCat-Video
- Long video generation: It was pretrained on the video continuation task and can generate videos several minutes long without color drift or quality degradation.
- Unified multi-task architecture: It unifies text-to-video, image-to-video, and video continuation tasks within a single video generation framework, so only one model is needed to complete all tasks.
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Efficient inference: Through a coarse-to-fine generation strategy and Block Sparse Attention technology, it can generate 720p, 30fps videos within a few minutes.
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Multi-reward reinforcement learning optimization: With multi-reward Group Relative Policy Optimization (GRPO), it demonstrates performance comparable to leading open-source video generation models and the latest commercial solutions on internal and public benchmarks.
Technical Principles of LongCat-Video
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Unified architecture: It adopts a single video generation framework that integrates multiple tasks such as text-to-video, image-to-video, and video continuation, and achieves efficient processing of different tasks through a shared model architecture and parameters.
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Long video generation technology: It is pretrained on the video continuation task and uses specific training strategies and algorithms to enable the model to generate long videos lasting several minutes while maintaining the coherence of video content and stability of quality.
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Efficient inference strategy: It uses a coarse-to-fine generation strategy, first generating a rough framework of the video and then gradually refining the details, combined with Block Sparse Attention technology, to improve the model's inference efficiency when generating high-resolution videos and shorten generation time.
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Multi-reward reinforcement learning optimization: With the multi-reward Group Relative Policy Optimization (GRPO) method, the model is optimized and trained to achieve better performance across multiple dimensions such as text alignment, visual quality, and motion quality, improving the overall quality of generated videos.
Project Address of LongCat-Video
- Project official website：https://meituan-longcat.github.io/LongCat-Video/
- Github repository：https://github.com/meituan-longcat/LongCat-Video
- HuggingFace model library：https://huggingface.co/meituan-longcat/LongCat-Video
Application Scenarios of LongCat-Video
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Content creation: It helps creators quickly generate video materials, such as advertising videos, short videos, animations, etc., improving creation efficiency.
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Video continuation: It generates follow-up content for existing video clips, for scenarios such as story expansion and video editing.
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Education and training: It generates instructional videos, demonstration videos, etc., assisting teaching and training processes and enhancing the learning experience.
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Entertainment and gaming: It generates dynamic scenes or character animations in game development, improving the game's visual effects and immersion.
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Intelligent customer service and virtual assistants: Generate video responses to provide users with a more intuitive interactive experience.
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Creative Design: Assist designers in video concept design, quickly presenting creative ideas.