EmoZone-Talker: 基于面部动作单元的音频驱动3DGS说话人头部的区域语义控制
EmoZone-Talker: Regional Semantic Control of Audio-Driven 3DGS Talking Heads via Facial Action Units
AI总结：
提出EmoZone-Talker框架，通过区域解耦和时序建模解决音频与表情信号的冲突，实现精细、可解释的面部表情控制。
摘要：
3D高斯泼溅（3DGS）在高保真说话头部合成方面显示出巨大潜力。然而，由于语音驱动的面部动态与显式表情信号之间的内在冲突，实现细粒度、可解释且可编辑的面部表情控制仍然具有根本性挑战。现有方法依赖隐式多模态融合，导致空间纠缠和时间不稳定性。我们提出EmoZone-Talker，一种新颖的框架，将音频驱动的面部动画重新表述为跨模态冲突下的结构化时空协调问题。我们的方法引入了面部运动的显式空间解缠和时序动态建模。具体来说，我们提出了具有优先注意力偏好的协同区域（SZ-PAB），通过解剖先验引导的区域约束显式解耦模态贡献，以及通道独立的时间AU编码器（CIT-AE）来建模时间连贯的AU动态。通过将这些表示集成到3D高斯变形中，EmoZone-Talker实现了对面部表情的精确和可解释控制。大量实验表明，我们的方法提高了表情可控性和真实感，在上脸准确性和时间连贯性方面取得了显著提升，同时保持了高渲染质量和准确的唇形同步。代码将公开发布以促进可重复性和进一步研究。
英文摘要：
3D Gaussian Splatting (3DGS) has shown strong potential for high-fidelity talking head synthesis. However, enabling fine-grained, interpretable, and editable facial expression control remains fundamentally challenging due to intrinsic conflicts between speech-driven facial dynamics and explicit expression signals. Existing methods rely on implicit multimodal fusion, leading to spatial entanglement and temporal instability. We present EmoZone-Talker, a novel framework that reformulates audio-driven facial animation as a structured spatial-temporal coordination problem under cross-modal conflicts. Our approach introduces an explicit spatial disentanglement and temporal dynamics modeling of facial motion. Specifically, we propose Synergy Zones with Prioritized Attention Bias (SZ-PAB) to explicitly decouple modality contributions via region-wise constraints guided by anatomical priors, and a Channel-Independent Temporal AU Encoder (CIT-AE) to model temporally coherent AU dynamics. By integrating these representations into 3D Gaussian deformation, EmoZone-Talker enables precise and interpretable control over facial expressions. Extensive experiments demonstrate that our method improves expression controllability and realism, with notable gains in upper-face accuracy and temporal coherence, while preserving high rendering quality and accurate lip synchronization. Code will be publicly released to facilitate reproducibility and further research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
