Recent years have witnessed significant progress in audio-driven human animation. However, critical challenges remain in (i) generating highly dynamic videos while preserving character consistency, (ii) achieving precise emotion alignment between characters and audio, and (iii) enabling multi-character audio-driven animation. To address these challenges, we propose HunyuanVideo-Avatar, a multimodal diffusion transformer (MM-DiT)-based model capable of simultaneously generating dynamic, emotion-controllable, and multi-character dialogue videos. Concretely, HunyuanVideo-Avatar introduces three key innovations: (i) A character image injection module is designed to replace the conventional addition-based character conditioning scheme, eliminating the inherent condition mismatch between training and inference. This ensures the dynamic motion and strong character consistency; (ii) An Audio Emotion Module (AEM) is introduced to extract and transfer the emotional cues from an emotion reference image to the target generated video, enabling fine-grained and accurate emotion style control; (iii) A Face-Aware Audio Adapter (FAA) is proposed to isolate the audio-driven character with latent-level face mask, enabling independent audio injection via cross-attention for multi-character scenarios. These innovations empower HunyuanVideo-Avatar to surpass state-of-the-art methods on benchmark datasets and a newly proposed wild dataset, generating realistic avatars in dynamic, immersive scenarios. The source code and model weights will be released publicly.
HunyuanVideo-Avatar Overall Architecture
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We propose HunyuanVideo-Avatar, a multi-modal diffusion transformer(MM-DiT)-based model capable of generating dynamic, emotion-controllable, and multi-character dialogue videos.
🎉 HunyuanVideo-Avatar Key Features
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High-Dynamic and Emotion-Controllable Video Generation
HunyuanVideo-Avatar supports animating any input avatar images to high-dynamic and emotion-controllable videos with simple audio conditions. Specifically, it takes as input multi-style avatar images at arbitrary scales and resolutions. The system supports multi-style avatars encompassing photorealistic, cartoon, 3D-rendered, and anthropomorphic characters. Multi-scale generation spanning portrait, upper-body and full-body. It generates videos with high-dynamic foreground and background, achieving superior realistic and naturalness. In addition, the system supports controlling facial emotions of the characters conditioned on input audio.
Various Applications
HunyuanVideo-Avatar supports various downstream tasks and applications. For instance, the system generates talking avatar videos, which could be applied to e-commerce, online streaming, social media video production, etc. In addition, its multi-character animation feature enlarges the application such as video content creation, editing, etc.
🚀 Parallel Inference on Multiple GPUs
For example, to generate a video with 8 GPUs, you can use the following command:
If you find HunyuanVideo-Avatar useful for your research and applications, please cite using this BibTeX:
BibTeX
1@misc{hu2025HunyuanVideo-Avatar,
2 title={HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters},
3 author={Yi Chen and Sen Liang and Zixiang Zhou and Ziyao Huang and Yifeng Ma and Junshu Tang and Qin Lin and Yuan Zhou and Qinglin Lu},
4 year={2025},
5 eprint={2505.20156},
6 archivePrefix={arXiv},
7 primaryClass={cs.CV},
8 url={https://arxiv.org/pdf/2505.20156},
9}
