What is LongCat-Video-Avatar?

LongCat-Video-Avatar is an open-source unified model from Meituan's LongCat team for expressive audio-driven character animation, supporting AT2V, ATI2V, and video continuation with natural lip-sync and dynamics.

When was LongCat-Video-Avatar released?

It was released on December 16, 2025, with model weights, code, and technical report made public on Hugging Face and GitHub.

Is LongCat-Video-Avatar free to use?

Yes, it's completely free and open-source under MIT license, with full model weights and inference code available for download and modification.

What hardware is needed for LongCat-Video-Avatar?

It requires powerful multi-GPU setup (e.g., A100/H100) with PyTorch 2.6+, FlashAttention, and high VRAM for efficient inference, especially long videos.

Does LongCat-Video-Avatar support multi-person generation?

Yes, it handles both single-person and multi-character/avatar scenarios with consistent identity and natural interactions.

Where can I download LongCat-Video-Avatar?

Model weights are on Hugging Face at meituan-longcat/LongCat-Video-Avatar; code and report on GitHub meituan-longcat/LongCat-Video.

What license does LongCat-Video-Avatar use?

It is released under the MIT License, allowing free use, modification, and commercial applications (with trademark/patent caveats).

LongCat-Video-Avatar

From Meituan

Unified Open-Source Audio-Driven Avatar Animation Model – Expressive Talking Heads with Natural Dynamics and Long-Sequence Consistency

Video & Animation

16 Dec 2025

N/A

0.0

Pricing Model

Free

Starting Price

$0/Month

👁 36

About This AI

LongCat-Video-Avatar is an advanced open-source model from Meituan’s LongCat team, released in December 2025, designed for highly expressive and dynamic audio-driven character animation.

Built upon the LongCat-Video foundation, it uses a unified Diffusion Transformer (DiT) architecture to support multiple native tasks: Audio-Text-to-Video (AT2V), Audio-Text-Image-to-Video (ATI2V), and Video Continuation.

The model excels at generating realistic talking-head videos with accurate lip-sync, natural facial expressions, body movements, and consistent identity preservation across long sequences.

Key innovations include Cross-Chunk Latent Stitching to prevent pixel degradation and error accumulation in extended generations, Reference Skip Attention to maintain character identity without excessive leakage, and disentangled unconditional guidance for decoupling speech from motion.

It handles single-stream and multi-stream audio inputs, supports single- and multi-person scenarios, and produces high-quality outputs at 480p or 720p resolutions.

Fully MIT-licensed with model weights, inference code, and technical report available on Hugging Face, it requires significant GPU resources (e.g., multi-GPU setup with PyTorch 2.6+, FlashAttention) for efficient inference.

Best suited for researchers, developers, and creators building lifelike virtual avatars, talking heads, or long-form animated content with audio synchronization.

The model has gained attention in the open-source community with hundreds of downloads and positive feedback for its realism in human dynamics and lip synchronization.

Key Features

Unified multi-task architecture: Supports AT2V, ATI2V, and Video Continuation in a single model
Audio-driven animation: Generates expressive facial expressions, lip-sync, and natural body dynamics from audio input
Long-sequence consistency: Cross-Chunk Latent Stitching prevents degradation and error accumulation in extended videos
Identity preservation: Reference Skip Attention maintains character consistency without excessive image leakage
Disentangled guidance: Decouples speech-driven motion from unconditional priors for better control
Single and multi-person support: Handles scenarios with one or multiple characters
Multi-stream audio compatibility: Processes single or multiple audio inputs seamlessly
High-resolution output: Generates 480p or 720p videos with configurable quality
Efficient inference options: Supports FlashAttention-2/3, context parallel processing for multi-GPU
Open-source ecosystem: MIT license with full code, weights, and technical report on Hugging Face

Price Plans

Free ($0): Completely open-source model weights, code, and inference tools under MIT license with no usage fees
Cloud/Hosted (Custom): Potential costs for running on cloud GPUs (e.g., RunPod, Vast.ai) or enterprise deployment

Pros

Highly expressive and realistic: Delivers natural human dynamics, lip-sync, and facial expressions in audio-driven videos
Strong long-video handling: Maintains quality and consistency in extended generations via innovative stitching
Fully open-source: MIT license allows free use, modification, and commercial applications
Multi-task versatility: One model covers AT2V, ATI2V, and continuation without separate fine-tunes
Community traction: Positive reception in open-source AI circles with growing downloads and integrations
Technical sophistication: Addresses key issues like identity drift and stiff motion effectively
Research-friendly: Accompanied by detailed technical report and eval benchmarks

Cons

High hardware requirements: Needs powerful multi-GPU setup (e.g., A100/H100) for reasonable inference speed
Complex setup: Requires specific PyTorch version, FlashAttention, and dependencies like librosa/ffmpeg
Resource-intensive: Large model size (likely billions of parameters) demands significant VRAM
No hosted demo: Primarily local/offline use; no easy web interface or Spaces demo mentioned
Limited accessibility: Steep learning curve for non-experts; best for developers/researchers
Potential artifacts: Long generations or complex audio may still show minor inconsistencies
Recent release: Community tools, fine-tunes, and integrations still emerging

Use Cases

Talking head generation: Create lifelike virtual avatars from audio for presentations or videos
Multi-character animation: Animate scenes with multiple people synced to dialogue
Video continuation: Extend existing avatar clips while preserving identity and motion
Research in audio-visual synthesis: Experiment with expressive long-form human animation
Content creation tools: Build custom AI avatars for apps, games, or virtual assistants
Accessibility and education: Generate sign-language or dubbed avatar videos from audio
Entertainment prototypes: Prototype animated characters for films, ads, or social media

Target Audience

AI researchers and developers: Experimenting with advanced audio-driven video models
Content creators and animators: Building realistic talking avatars or extensions
Virtual human application builders: For chatbots, virtual assistants, or metaverse projects
Open-source enthusiasts: Using MIT-licensed models for custom projects
Academic teams: Studying expressive animation, lip-sync, and long-sequence generation
Tech companies: Integrating avatar tech into products or prototypes

How To Use

Clone repository: git clone --single-branch --branch main https://github.com/meituan-longcat/LongCat-Video
Set up environment: Create conda env with Python 3.10, install PyTorch 2.6+cu124, FlashAttention-2, and requirements.txt
Download model: Use huggingface-cli download meituan-longcat/LongCat-Video-Avatar --local-dir ./weights/LongCat-Video-Avatar
Prepare input: Create JSON config with audio path, text prompt, optional reference image
Run inference: Use torchrun with multi-GPU for AT2V/ATI2V, e.g., run_demo_avatar_single_audio_to_video.py
Adjust parameters: Set resolution (480/720), context_parallel_size, and other flags for quality/speed
View output: Generated video saved to output directory; iterate with different configs

How we rated LongCat-Video-Avatar

Performance: 4.6/5
Accuracy: 4.7/5
Features: 4.8/5
Cost-Efficiency: 5.0/5
Ease of Use: 4.0/5
Customization: 4.5/5
Data Privacy: 4.9/5
Support: 4.2/5
Integration: 4.4/5
Overall Score: 4.6/5