LongCat-Video

LongCat-Video, a foundational 13.6-billion parameter video generation model developed by Meituan, redefines the creation of dynamic media by integrating three core generation tasks into a single, cohesive architecture. This model addresses the traditional fragmentation of video AI, enabling users to seamlessly generate, animate, and extend content with unparalleled coherence. For developers, researchers, and creative professionals, LongCat-Video offers a robust, efficient, and highly flexible platform for advanced visual simulation.

Key Features

LongCat-Video is built upon a unified framework designed for stability, efficiency, and continuous world modeling.

1. ⚙️ Unified Multi-Task Architecture

Unlike traditional systems that require separate models for different tasks (e.g., Image-to-Video and Text-to-Video), LongCat-Video utilizes a single, unified architecture. This design allows the model to simultaneously support Text-to-Video, Image-to-Video, and Video-Continuation, facilitating knowledge sharing across modalities and significantly enhancing the stability and consistency of the generated visual-semantic mapping.

2. 🎬 Native Long-Video Continuation

LongCat-Video is natively pre-trained on the Video-Continuation task, allowing it to generate minute-level videos while maintaining stable color, lighting, and consistent motion logic across the entire duration. This overcomes the major limitation of older models where long videos were merely stitched short clips, often resulting in jarring light shifts, flickering, or discontinuous action.

3. ⚡ High-Efficiency Inference Pipeline

Designed for practical deployment, LongCat-Video can generate high-quality 720p, 30fps video in a matter of minutes. This efficiency is achieved through a coarse-to-fine generation strategy—starting with a lower-resolution draft and using a refinement expert model—and leveraging advanced techniques like Block Sparse Attention to accelerate high-resolution processing.

4. ✨ Multi-Reward Performance Optimization

To ensure output quality meets real-world standards, the model is trained using a sophisticated Multi-reward Reinforcement Learning from Human Feedback (GRPO) framework. This strategy optimizes for three critical, simultaneous metrics: Text Alignment, Visual Quality, and Motion Coherence, ensuring the resulting video is not just visually appealing, but also logically sound and faithful to the source prompt.

Use Cases

LongCat-Video’s unique capabilities make it suitable for applications demanding high consistency and continuity.

• Continuous Storyboarding and Pre-visualization: You can input a detailed script or scene description (Text-to-Video) and then use Video-Continuation to extend the sequence, generating cohesive, minute-long animated storyboards for film, game development, or advertising concepts without worrying about mid-scene discontinuity.

• Bringing Still Assets to Life: Transform static images into dynamic, high-quality video sequences (Image-to-Video). This is ideal for quickly animating product mockups, architectural visualizations, or character concepts, providing a complete sense of movement and environment from a single source image.

• Seamless Footage Extension and Simulation: Researchers and developers can utilize the Video-Continuation feature to test hypothetical scenarios or extend existing short video clips with logically plausible, continuous footage, making it a foundational tool for early-stage "World Model" development and simulation.

Why Choose LongCat-Video?

LongCat-Video offers substantial advantages over previous generations of video synthesis models, focusing on efficiency, stability, and depth of understanding.

• Superior Parameter-to-Performance Ratio: At 13.6B parameters, LongCat-Video demonstrates subjective quality (MOS scores) that meets or exceeds the performance of certain open-source models nearly twice its size (28B class). This means you benefit from a significantly lighter, faster, and more memory-efficient model without compromising output quality.

• True Continuity, Not Just Stitching: The native pre-training for Video-Continuation fundamentally changes how long videos are generated. Instead of relying on post-processing to hide discontinuities, LongCat-Video models the temporal dynamics and causality from the start, delivering a genuinely continuous visual narrative.

• Open and Accessible Commercial Use: Released under the permissive MIT License, LongCat-Video allows individuals and enterprises the freedom to use and adapt the model for commercial applications, fostering broader innovation and integration into diverse workflows.

Conclusion

LongCat-Video stands as a critical advancement in generative AI, offering a single, powerful solution for text, image, and video-based synthesis. By prioritizing unified architecture and genuine long-form continuity, it delivers highly stable, efficient, and coherently modeled video content. Explore how LongCat-Video can elevate your creative, research, or development projects by providing a reliable engine for continuous visual world simulation.