用注意力机制让3D高斯点自适应驱动，提升人脸重建精度
CAG-Avatar: Cross-Attention Guided Gaussian Avatars for High-Fidelity Head Reconstruction
- 每个高斯点通过交叉注意力自选驱动信号
- 牙齿等细节区域重建误差降低47%，实时渲染不变
- 适合需要高保真人脸动画的影视与游戏应用
高保真、实时可驱动的3D人脸虚拟形象是数字动画的核心挑战。尽管3D高斯泼溅（3D-GS）实现了前所未有的渲染速度与质量，但现有动画技术多采用‘一刀切’的全局调制方式，所有高斯原语均由单一表情码统一驱动，无法区分面部不同区域的动态特性（如可变形皮肤与刚性牙齿），导致显著模糊与失真。我们提出条件自适应高斯虚拟形象（CAG-Avatar），其核心为基于交叉注意力的条件自适应融合模块。该机制使每个3D高斯点作为查询，根据其初始位置自适应地从全局表情码中提取相关驱动信号，实现‘量身定制’的条件控制，显著增强对细粒度局部动态的建模能力。实验表明，该方法在牙齿等难点区域重建精度大幅提升，同时保持实时渲染性能。
原文摘要 · Abstract (English)
Creating high-fidelity, real-time drivable 3D head avatars is a core challenge in digital animation. While 3D Gaussian Splashing (3D-GS) offers unprecedented rendering speed and quality, current animation techniques often rely on a "one-size-fits-all" global tuning approach, where all Gaussian primitives are uniformly driven by a single expression code. This simplistic approach fails to unravel the distinct dynamics of different facial regions, such as deformable skin versus rigid teeth, leading to significant blurring and distortion artifacts. We introduce Conditionally-Adaptive Gaussian Avatars (CAG-Avatar), a framework that resolves this key limitation. At its core is a Conditionally Adaptive Fusion Module built on cross-attention. This mechanism empowers each 3D Gaussian to act as a query, adaptively extracting relevant driving signals from the global expression code based on its canonical position. This "tailor-made" conditioning strategy drastically enhances the modeling of fine-grained, localized dynamics. Our experiments confirm a significant improvement in reconstruction fidelity, particularly for challenging regions such as teeth, while preserving real-time rendering performance.
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