ActAvatar: Temporally-Aware Precise Action Control for Talking Avatars

• URL: http://arxiv.org/abs/2512.19546v1
• Date: Mon, 22 Dec 2025 16:28:27 GMT
• Title: ActAvatar: Temporally-Aware Precise Action Control for Talking Avatars
• Authors: Ziqiao Peng, Yi Chen, Yifeng Ma, Guozhen Zhang, Zhiyao Sun, Zixiang Zhou, Youliang Zhang, Zhengguang Zhou, Zhaoxin Fan, Hongyan Liu, Yuan Zhou, Qinglin Lu, Jun He,
• Abstract summary: ActAvatar is a framework that achieves phase-level precision in action control through textual guidance.<n>Phase-Aware Cross-Attention (PACA) decomposes prompts into a global base block and temporally-anchored phase blocks.<n>Progressive Audio-Visual Alignment aligns modality influence with the hierarchical feature learning process.<n>Two-stage training strategy injects action control through fine-tuning on structured annotations.
• Score: 28.337100940626573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite significant advances in talking avatar generation, existing methods face critical challenges: insufficient text-following capability for diverse actions, lack of temporal alignment between actions and audio content, and dependency on additional control signals such as pose skeletons. We present ActAvatar, a framework that achieves phase-level precision in action control through textual guidance by capturing both action semantics and temporal context. Our approach introduces three core innovations: (1) Phase-Aware Cross-Attention (PACA), which decomposes prompts into a global base block and temporally-anchored phase blocks, enabling the model to concentrate on phase-relevant tokens for precise temporal-semantic alignment; (2) Progressive Audio-Visual Alignment, which aligns modality influence with the hierarchical feature learning process-early layers prioritize text for establishing action structure while deeper layers emphasize audio for refining lip movements, preventing modality interference; (3) A two-stage training strategy that first establishes robust audio-visual correspondence on diverse data, then injects action control through fine-tuning on structured annotations, maintaining both audio-visual alignment and the model's text-following capabilities. Extensive experiments demonstrate that ActAvatar significantly outperforms state-of-the-art methods in both action control and visual quality.

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