EAI-Avatar: Emotion-Aware Interactive Talking Head Generation

• URL: http://arxiv.org/abs/2508.18337v2
• Date: Wed, 24 Sep 2025 06:28:07 GMT
• Title: EAI-Avatar: Emotion-Aware Interactive Talking Head Generation
• Authors: Haijie Yang, Zhenyu Zhang, Hao Tang, Jianjun Qian, Jian Yang,
• Abstract summary: We propose EAI-Avatar, a novel emotion-aware talking head generation framework for dyadic interactions.<n>Our method produces temporally consistent virtual avatars with rich emotional variations that seamlessly transition between speaking and listening states.
• Score: 35.56554951482687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative models have advanced rapidly, enabling impressive talking head generation that brings AI to life. However, most existing methods focus solely on one-way portrait animation. Even the few that support bidirectional conversational interactions lack precise emotion-adaptive capabilities, significantly limiting their practical applicability. In this paper, we propose EAI-Avatar, a novel emotion-aware talking head generation framework for dyadic interactions. Leveraging the dialogue generation capability of large language models (LLMs, e.g., GPT-4), our method produces temporally consistent virtual avatars with rich emotional variations that seamlessly transition between speaking and listening states. Specifically, we design a Transformer-based head mask generator that learns temporally consistent motion features in a latent mask space, capable of generating arbitrary-length, temporally consistent mask sequences to constrain head motions. Furthermore, we introduce an interactive talking tree structure to represent dialogue state transitions, where each tree node contains information such as child/parent/sibling nodes and the current character's emotional state. By performing reverse-level traversal, we extract rich historical emotional cues from the current node to guide expression synthesis. Extensive experiments demonstrate the superior performance and effectiveness of our method.

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