HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters

• URL: http://arxiv.org/abs/2505.20156v2
• Date: Tue, 03 Jun 2025 15:15:31 GMT
• Title: HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters
• Authors: Yi Chen, Sen Liang, Zixiang Zhou, Ziyao Huang, Yifeng Ma, Junshu Tang, Qin Lin, Yuan Zhou, Qinglin Lu,
• Abstract summary: HunyuanVideo-Avatar is a model capable of simultaneously generating dynamic, emotion-controllable, and multi-character dialogue videos.<n>A character image injection module is designed to replace the conventional addition-based character conditioning scheme.<n>An Audio Emotion Module (AEM) is introduced to extract and transfer the emotional cues from an emotion reference image to the target generated video.<n>A Face-Aware Audio Adapter (FAA) is proposed to isolate the audio-driven character with latent-level face mask.
• Score: 14.594698765723756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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