We are excited to announce the release of LongCat-Video-Avatar, a unified model that delivers expressive and highly dynamic audio-driven character animation, supporting native tasks including Audio-Text-to-Video, Audio-Text-Image-to-Video, and Video Continuation with seamless compatibility for both single-stream and multi-stream audio inputs.
Key Features
🌟 Support Multiple Generation Modes: One unified model can be used for audio-text-to-video (AT2V) generation, audio-text-image-to-video (ATI2V) generation, and Video Continuation.
🌟 Natural Human Dynamics: The disentangled unconditional guidance is designed to effectively decouple speech signals from motion dynamics for natural behavior.
🌟 Avoid Repetitive Content: The reference skip attention is adopted to strategically incorporates reference cues to preserve identity while preventing excessive conditional image leakage.
🌟 Alleviate Error Accumulation from VAE: Cross-Chunk Latent Stitching is designed to eliminates redundant VAE decode-encode cycles to reduce pixel degradation in long sequences.
The following videos showcase example generations from our model and have been compressed for easier viewing.
📊 Human Evaluation
Human evaluation on naturalness and realism of the synthesized videos. The benchmark EvalTalker [1] contains more than 400 testing samples with different difficulty levels for evaluating the single and multiple human video generation.
LongCat-Video-Avatar
Reference:
[1] Zhou Y, Zhu X, Ren S, et al. EvalTalker: Learning to Evaluate Real-Portrait-Driven Multi-Subject Talking Humans[J]. arXiv preprint arXiv:2512.01340, 2025.
⚖️ License Agreement
The model weights are released under the MIT License.
Any contributions to this repository are licensed under the MIT License, unless otherwise stated. This license does not grant any rights to use Meituan trademarks or patents.
This model has not been specifically designed or comprehensively evaluated for every possible downstream application.
Developers should take into account the known limitations of large language models, including performance variations across different languages, and carefully assess accuracy, safety, and fairness before deploying the model in sensitive or high-risk scenarios.
It is the responsibility of developers and downstream users to understand and comply with all applicable laws and regulations relevant to their use case, including but not limited to data protection, privacy, and content safety requirements.
Nothing in this Model Card should be interpreted as altering or restricting the terms of the MIT License under which the model is released.
📖 Citation
We kindly encourage citation of our work if you find it useful.
