HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters
Journal:
arXiv
Published Date:
May 26, 2025
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.
