LongCat-Video: Meituan’s Groundbreaking Step Toward Efficient Long Video Generation with AI

In the rapidly advancing field of generative AI, the ability to create realistic, coherent, and high-quality videos from text or images has become one of the most sought-after goals. Meituan, one of the leading technology innovators in China, has made a remarkable stride in this domain with its latest open-source model — LongCat-Video. Designed as a foundational video generation framework with 13.6 billion parameters, LongCat-Video demonstrates exceptional versatility across Text-to-Video (T2V), Image-to-Video (I2V) and Video-Continuation tasks.

This model marks a new milestone in the evolution of multimodal AI systems, combining efficiency, scalability, and quality in a single architecture. With its strong performance across benchmarks and capability for long-duration video synthesis, LongCat-Video represents an important step toward the creation of “world models” — AI systems capable of understanding and generating complex dynamic environments.

Understanding LongCat-Video

LongCat-Video is not just another generative model; it’s a unified video generation system that brings together different video creation tasks under one architecture. Whether the input is text, an image or an existing video clip, LongCat-Video can generate new frames, extend scenes or produce entirely new sequences with high visual coherence.

With 13.6 billion parameters, the model leverages dense architecture not mixture-of-experts (MoE) ensuring that all parameters contribute actively to inference. This leads to consistent and reliable performance across tasks, unlike many MoE-based systems that activate only a subset of parameters at a time.

Moreover, LongCat-Video employs a coarse-to-fine generation strategy along both temporal and spatial axes. This innovative approach allows it to create long videos sometimes lasting several minutes with minimal color drifting or quality degradation, issues that have plagued earlier video synthesis models.

Explore Github here

Key Features and Capabilities

1. Unified Architecture for Multiple Tasks

LongCat-Video’s biggest strength lies in its ability to handle multiple video generation tasks natively. Instead of training separate models for Text-to-Video, Image-to-Video, and Video-Continuation, Meituan has developed a single architecture capable of performing all three seamlessly. This not only simplifies the development pipeline but also ensures shared learning across modalities, improving overall generalization.

2. Long-Form Video Generation

A defining capability of LongCat-Video is its long video generation feature. Thanks to its pretraining on Video-Continuation tasks, the model can generate extended sequences without noticeable temporal inconsistencies. This opens new possibilities for creating narrative videos, movie scenes and educational content where maintaining coherence across time is crucial.

3. Efficient Inference

Despite its large size, LongCat-Video is optimized for performance. It integrates Block Sparse Attention and FlashAttention-2 drastically improving inference speed. As a result, the model can generate 720p 30fps videos within minutes making it practical even for high-resolution and long-duration video outputs.

4. Reinforcement Learning with Multi-Reward Optimization

Meituan has employed Group Relative Policy Optimization (GRPO) – a reinforcement learning technique with multiple reward signals to align the model’s outputs with human preferences. This multi-reward system helps balance text alignment, motion quality and visual realism ensuring that videos not only look great but also accurately follow user prompts.

Performance and Evaluation

In extensive internal evaluations, LongCat-Video achieved results comparable to some of the most advanced open-source and commercial models in the field including PixVerse-V5, Veo3 and Wan 2.2.

For Text-to-Video generation, the model achieved an overall MOS (Mean Opinion Score) of 3.38, demonstrating strong consistency across text alignment, motion fluidity and visual quality. Similarly, in Image-to-Video tasks, it maintained competitive alignment scores and superior visual fidelity outperforming other open-source frameworks in efficiency and coherence.

These results affirm LongCat-Video’s position as one of the leading open-source video generation solutions available today not just for short clips but for extended high-quality video sequences.

Getting Started with LongCat-Video

Developers can easily experiment with LongCat-Video through its Hugging Face model repository. Once downloaded, it can be used for various tasks such as:

Text-to-Video: Generating video content directly from textual descriptions.
Image-to-Video: Bringing static images to life with dynamic motion.
Video-Continuation: Extending existing videos seamlessly.
Interactive Video Generation: Creating content interactively through Streamlit-based demos.

The installation process is straightforward, using PyTorch, FlashAttention and other Python-based dependencies. The repository also supports multi-GPU inference allowing faster generation for research and production environments.

Open-Source Philosophy and Community Collaboration

LongCat-Video is released under the MIT License, promoting open research and community contribution. Meituan encourages developers and researchers to experiment, build upon and optimize the model for their own applications.

The community has already started developing complementary tools such as CacheDiT which provides Fully Cache Acceleration through DBCache and TaylorSeer delivering nearly 1.7x speedup in performance with minimal precision loss. This ecosystem of collaboration ensures continuous improvement and innovation within the LongCat-Video framework.

Applications and Future Impact

LongCat-Video’s potential extends far beyond entertainment. Its ability to generate coherent, extended and realistic videos opens possibilities across various industries, including:

Advertising and Marketing: Automated creation of campaign visuals and dynamic storytelling.
Education: Generating visual explanations, demonstrations and historical recreations.
Film Production: Rapid prototyping of scenes, visual effects and animations.
Virtual Reality and Simulation: Generating immersive environments for training and design.

Moreover, its unified design hints at Meituan’s larger vision — the development of world models capable of understanding and generating complex real-world scenarios. As such, LongCat-Video stands not only as a video generator but as an early foundation for AI systems that perceive and recreate the physical world.

Conclusion

LongCat-Video represents a significant breakthrough in AI-driven video generation. By unifying diverse tasks, enabling efficient long-form synthesis, and maintaining open accessibility, Meituan has set a new benchmark for what’s possible in generative video modeling. Its combination of advanced architecture, optimization techniques, and real-world applicability makes it a cornerstone in the evolution of multimodal AI systems.

As AI continues to merge the boundaries between creativity and computation, models like LongCat-Video pave the way for a future where high-quality video generation is not just a technical challenge but a creative tool accessible to all.

Follow us for cutting-edge updates in AI & explore the world of LLMs, deep learning, NLP and AI agents with us.

References