AI-powered long-form video processing and analysis.

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December 18, 2025

LongCat Video

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AI-powered long-form video processing and analysis.

Video Generation Text To Video Conversion Image-to-video Sequence Video Continuation Audio-Text-Image-to-Video Generation Long-video Creation

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LongCat-Video is a comprehensive video generation model by meituan-longcat developed on GitHub. This AI tool is designed to execute multiple tasks within one video generation framework, including converting text to video, translating images into video sequences, and generating continuation in videos.

One key feature of LongCat-Video is in its ability to efficiently create long videos without reduction in quality or noticeable color drifting. It follows a coarse-to-fine generation strategy along both temporal and spatial axes to enhance efficiency, especially at high resolutions.

The model was trained through Group Relative Policy Optimization, which incorporates multi-reward Real-life High Fidelity (RLHF) and ensures competitive performance across multiple metrics when compared to leading open-source and commercial video generation models.

Moreover, the AI model has launched an expressive audio-driven character animation feature, known as LongCat-Video-Avatar, which can natively handle tasks such as Audio-Text-to-Video conversion, Audio-Text-Image-to-Video generation, and Video Continuation.

It offers seamless compatibility for both single-stream and multi-stream audio inputs. All technical reports, inference code, model weights, and project pages related to LongCat-Video are openly available on GitHub.

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December 17, 2025

Initial release of LongCat Video.

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Pros and Cons

Pros

Multitasking video generation

Text-to-video conversion

Image-to-video translation

Video continuation creation

Efficient long video creation

No quality reduction

Decreased color drifting

Coarse-to-fine generation strategy

Temporal and spatial efficiency

High resolution capability

Group Relative Policy Optimization

Real-life High Fidelity

Expressive audio-driven animation

Audio-Text-to-Video conversion

Audio-Text-Image-to-Video generation

Single-stream audio compatibility

Multi-stream audio compatibility

Open-source GitHub project

Inference code provided

Model weights accessible

Performance comparability

High fidelity across metrics

View 17 more pros

Cons

Complex installation process

Requires specific CUDA version

No model for xformers

Execution requires specific python version

Potential synchronization issues (audio-driven feature)

Prompt requirements for more natural movements

Repeated actions mitigation limitations

Super resolution only up to 720P

Requires equal-length audio clips for dual-audio mode

Efficiency enhanced only at high resolutions

View 5 more cons

Q&A

What is the aim of LongCat-Video?

LongCat-Video is designed to execute multiple tasks within one video generation framework. Its main purpose is to convert text to video, translate images into video sequences, and generate continuation in videos.

How does LongCat-Video convert text to video?

LongCat-Video's text-to-video conversion capability is powered by its advanced AI model trained on Group Relative Policy Optimization (GRPO). The exact specifics of the text-to-video conversion process are part of the model's proprietary design, but it employs a unifying architecture that handles multiple tasks, including text-to-video conversion, within a single framework.

What does LongCat-Video's feature of translating images into video sequences entail?

The translation of images into video sequences by LongCat-Video involves taking multiple image inputs and converting them into a continuous video sequence. It employs the trained AI model to enhance the continuity and flow between the image frames, effectively creating a video.

How does LongCat-Video generate continuation in videos?

LongCat-Video generates continuation in videos using its pre-trained AI model. It makes use of the input data and employs a strategy along both temporal and spatial axes to create a seamless continuation of the video according to the context provided.

Can LongCat-Video maintain video quality for long videos?

Yes, a key feature of LongCat-Video is in its ability to efficiently create long videos while maintaining the quality without any noticeable reduction in quality or color drifting.

What is LongCat-Video's coarse-to-fine generation strategy?

LongCat-Video's coarse-to-fine generation strategy involves initial generation of a video in a lower resolution or less detail (coarse), followed by progressively increasing the detail and resolution (fine), enhancing both the temporal and spatial efficiency of video generation.

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How was LongCat-Video trained?

LongCat-Video's training process involved the use of Group Relative Policy Optimization (GRPO), which incorporates multi-reward Real-life High Fidelity (RLHF), ensuring competitive performance across multiple metrics.

What is the function of the Group Relative Policy Optimization?

The Group Relative Policy Optimization used in training LongCat-Video contributes to optimizing the AI model’s predictive and generative performance. It enables the model to learn and improve from multiple rewards, strengthening its video generating and processing capabilities.

Why does LongCat-Video use Real-life High Fidelity in its training?

Real-life High Fidelity (RLHF) is used in LongCat-Video's training to ensure that the video generation and processing results it produces are of high quality and closely resemble real-world videos, enhancing the model's practical applicability and effectiveness.

How does LongCat-Video compare to other video generation models?

LongCat-Video performs competitively across multiple metrics when compared to leading open-source and commercial video generation models. However, the specifics of this comparison are dependent on individual performance metrics and may vary based on the requirements and context.

What is the LongCat-Video-Avatar feature?

LongCat-Video-Avatar is a feature launched in the AI model that enables the creation of expressive, audio-driven character animation. It enhances LongCat-Video's capabilities by adding an dynamic animation aspect to its video generation tasks.

What tasks can be handled natively by the LongCat-Video-Avatar feature?

The LongCat-Video-Avatar feature can natively handle tasks such as Audio-Text-to-Video conversion, Audio-Text-Image-to-Video generation, and Video Continuation.

What types of audio inputs does LongCat-Video support?

LongCat-Video supports both single-stream and multi-stream audio inputs, offering seamless compatibility for various audio input formats.

Where can I find technical reports related to LongCat-Video?

Technical reports related to LongCat-Video are openly available on GitHub. These include inference code, model weights, and project pages alongside the technical reports.

How can I get the model weights for LongCat-Video?

The model weights for LongCat-Video can be found on their GitHub project page. They provide detailed information about how the AI model was trained, the algorithms it uses, and its overall architecture and design.

What platforms support LongCat-Video?

LongCat-Video, developed by meituan-longcat, is supported on GitHub. It's an open-source project and its resources including model weights and code are available for use and contribution by the public.

How does LongCat-Video handle high resolution videos?

LongCat-Video handles high resolution videos effectively, especially at 720p and 30 fps. It utilises a coarse-to-fine generation strategy for efficiency, and employs Block Sparse Attention to enhance performance at high resolutions.

What is the advantage of using LongCat-Video?

The advantage of using LongCat-Video lies in its multifaceted capabilities, which include converting text to videos, translating images to video sequences, and generating video continuation. It's also efficient in creating high-resolution long videos. Another benefit is its open availability on GitHub, which allows developers to freely access and benefit from the existing codebase, model weights, and associated technical documentation.

Will LongCat-Video be updated or improved in the future?

Updates or improvements to LongCat-Video would depend on its developers. While the current stated feature set is comprehensive, advancements in AI and video technology could prompt future updates. Any such changes would be reflected in their GitHub repository.

Is the code of LongCat-Video available to the public?

Yes, LongCat-Video's code, along with its technical reports, inference code, model weights, and project pages, are openly available on GitHub, allowing anyone to access, learn from, and contribute to it.

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